(upbeat techno music) >> Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions, and they were largely confined to regulated industries that had to comply with public policy mandates. But as the cloud went mainstream the tech giants showed us how valuable data could become, and the value proposition for data quality and trust, it evolved from primarily a compliance driven issue, to becoming a linchpin of competitive advantage. But, data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper-specialized skills, to develop data architectures and processes, to serve the myriad data needs of organizations. And it resulted in a lot of frustration, with data initiatives for most organizations, that didn't have the resources of the cloud guys and the social media giants, to really attack their data problems and turn data into gold. This is why today, for example, there's quite a bit of momentum to re-thinking monolithic data architectures. You see, you hear about initiatives like Data Mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business users. You hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver, like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that but also, how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. In other words, while it's enticing to experiment, and run fast and loose with data initiatives, kind of like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated and intelligent. Governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is going to use data that is entrusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. Hello and welcome to theCUBE's coverage of Data Citizens made possible by Collibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Vellante and I'm one of the hosts of our program which is running in parallel to Data Citizens. Now at theCUBE we like to say we extract the signal from the noise, and over the next couple of days we're going to feature some of the themes from the keynote speakers at Data Citizens, and we'll hear from several of the executives. Felix Van de Maele, who is the co-founder and CEO of Collibra, will join us. Along with one of the other founders of Collibra, Stan Christiaens, who's going to join my colleague Lisa Martin. I'm going to also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Haslbeck. He's the Vice President of Data Quality at Collibra. He's an amazingly smart dude who founded Owl DQ, a company that he sold to Collibra last year. Now, many companies they didn't make it through the Hadoop era, you know they missed the industry waves and they became driftwood. Collibra, on the other hand, has evolved its business, they've leveraged the cloud, expanded its product portfolio and leaned in heavily to some major partnerships with cloud providers as well as receiving a strategic investment from Snowflake, earlier this year. So, it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. (upbeat rock music) Last year theCUBE covered Data Citizens, Collibra's customer event, and the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know starting with the Hadoop movement, we had Data lakes, we had Spark, the ascendancy of programming languages like Python, the introduction of frameworks like Tensorflow, the rise of AI, Low Code, No Code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives, and we said at the time, you know maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation. Meaning, making it easier for domain experts to both gain insights from data, trust the data, and begin to use that data in new ways, fueling data products, monetization, and insights. Data Citizens 2022 is back and we're pleased to have Felix Van de Maele who is the founder and CEO of Collibra. He's on theCUBE. We're excited to have you Felix. Good to see you again. >> Likewise Dave. Thanks for having me again. >> You bet. All right, we're going to get the update from Felix on the current data landscape, how he sees it why data intelligence is more important now than ever, and get current on what Collibra has been up to over the past year, and what's changed since Data citizens 2021, and we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends and we're not just snapping back to the 2010s, that's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s, from the previous decade, and what challenges does that bring for your customers? >> Yeah, absolutely, and and I think you said it well, Dave and the intro that, that rising complexity and fragmentation, in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use, has only gotten more more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under, respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well. Which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity, and fragmentation. So, it's become much more acute. And to your earlier point, we do live in a different world and and the past couple of years we could probably just kind of brute force it, right? We could focus on, on the top line, there was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, how do we truly get the value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale with data, not just from a a technology and infrastructure perspective, but how do we actually scale data from an organizational perspective, right? You said at the, the people and process, how do we do that at scale? And that's only, only, only becoming much more important, and we do believe that the, the economic environment that we find ourselves in today is going to be catalyst for organizations to really take that more seriously if, if, if you will, than they maybe have in the have in the past. >> You know, I don't know when you guys founded Collibra, if you had a sense as to how complicated it was going to get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >> Yeah, absolutely. We, we started Collibra in 2008. So, in some sense and the, the last kind of financial crisis and that was really the, the start of Collibra, where we found product market fit, working with large financial institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis. And kind of here we are again, in a very different environment of course 15 years, almost 15 years later, but data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So, what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it Data Citizens, we truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we still relatively early in that, in that journey. >> Well that's interesting, because you know, in my observation it takes 7 to 10 years to actually build a company, and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your current momentum? >> Yeah, absolutely. Again, there's a lot of tailwind organizations that are only maturing their data practices and we've seen that kind of transform or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world with its Adobe, Heineken, Bank of America and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in the, in the market with some of the cloud partners like Google, Amazon, Snowflake, Data Breaks, and and others, right? As those kind of new modern data infrastructures, modern data architectures, are definitely all moving to the cloud. A great opportunity for us, our partners, and of course our customers, to help them kind of transition to the cloud even faster. And so we see a lot of excitement and momentum there. We did an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course data quality isn't new but I think there's a lot of reasons why we're so excited about quality and observability now. One, is around leveraging AI machine learning again to drive more automation. And a second is that those data pipelines, that are now being created in the cloud, in these modern data architecture, architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously, has become absolutely critical so that they're really excited about, about that as well. And on the organizational side, I'm sure you've heard the term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believed in. Federated, focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations, and so that aligns really well with our vision and from a product perspective, we've seen a lot of momentum with our customers there as well. >> Yeah, you know, a couple things there. I mean, the acquisition of OwlDQ, you know Kirk Haslbeck and, and their team. It's interesting, you know the whole data quality used to be this back office function and and really confined to highly regulated industries. It's come to the front office, it's top of mind for Chief Data Officers. Data mesh, you mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So, let's chat a little bit about the, the products. We're going to go deeper into products later on, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the the under the covers in security, sort of making data more accessible for people, just dealing with workflows and processes, as you talked about earlier. Tell us a little bit about what you're introducing. >> Yeah, absolutely. We we're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission. Either customers are still start, are just starting on that, on that journey. We want to make it as easy as possible for the, for organization to actually get started, because we know that's important that they do. And for our organization and customers, that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again to make it easier for, really to, to accomplish that mission and vision around that Data Citizen, that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving, a lot of kind of ease of adoption, ease of use, but also then, how do we make sure that, as clear becomes this kind of mission critical enterprise platform, from a security performance, architecture scale supportability, that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme. From an innovation perspective, from a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One, is around data marketplace. Again, a lot of our customers have plans in that direction, How to make it easy? How do we make How do we make available to true kind of shopping experience? So that anybody in the organization can, in a very easy search first way, find the right data product, find the right dataset, that they can then consume. Usage analytics, how do you, how do we help organizations drive adoption? Tell them where they're working really well and where they have opportunities. Homepages again to, to make things easy for, for people, for anyone in your organization, to kind of get started with Collibra. You mentioned Workflow Designer, again, we have a very powerful enterprise platform, one of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a, a new Low-Code, No-Code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around Collibra protect, which in partnership with Snowflake, which has been a strategic investor in Collibra, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PIA data, is managed as a much more effective, effective rate. Really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily, and quickly, and widely as we can? Moving that to the cloud has been a big part of our strategy. So, we launch our data quality cloud product, as well as making use of those, those native compute capabilities and platforms, like Snowflake, Databricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down, so we're actually pushing down the computer and data quality, to monitoring into the underlying platform, which again from a scale performance and ease of use perspective, is going to make a massive difference. And then more broadly, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical, and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So that's a lot coming out, the team has been work, at work really hard, and we are really really excited about what we are coming, what we're bringing to market. >> Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you you talked about, you know, the marketplace, you know you think about Data Mesh, you think of data as product, one of the key principles, you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been, been so hard. So, how do you see sort of the future and, you know give us the, your closing thoughts please? >> Yeah, absolutely. And, and I think we we're really at a pivotal moment and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not going to fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to, deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can, as kind of our, as our mission. And so I'm really, really excited to see what we, what we are going to, how the marks are going to evolve over the next, next few quarters and years. I think the trend is clearly there. We talked about Data Mesh, this kind of federated approach focus on data products, is just another signal that we believe, that a lot of our organization are now at the time, they're understanding need to go beyond just the technology. I really, really think about how to actually scale data as a business function, just like we've done with IT, with HR, with sales and marketing, with finance. That's how we need to think about data. I think now is the time, given the economic environment that we are in, much more focus on control, much more focus on productivity, efficiency, and now is the time we need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >> Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much. Good luck in, in San Diego. I know you're going to crush it out there. >> Thank you Dave. >> Yeah, it's a great spot for an in-person event and and of course the content post-event is going to be available at collibra.com and you can of course catch theCUBE coverage at theCUBE.net and all the news at siliconangle.com. This is Dave Vellante for theCUBE, your leader in enterprise and emerging tech coverage. (upbeat techno music)

(upbeat music) >> Last year, the Cube covered Data Citizens, Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hadoop movement. We had data lakes, we had Spark, the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of AI, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights from data, trust the data, and begin to use that data in new ways, fueling data products, monetization, and insights. Data Citizens 2022 is back, and we're pleased to have Felix Van de Maele, who is the founder and CEO of Collibra. He's on the Cube. We're excited to have you, Felix. Good to see you again. >> Likewise Dave. Thanks for having me again. >> You bet. All right, we're going to get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever, and get current on what Collibra has been up to over the past year, and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear. And that's really true, as well, in the world of data. So what's different in your mind in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >> Yeah, absolutely. And I think you said it well, Dave, in the intro that rising complexity and fragmentation in the broader data landscape that hasn't gotten any better over the last couple of years. When we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use, has only gotten kind of more difficult. So that trend is continuing. I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under with respect to data, as data becomes more mission critical, as data becomes more impactful and important, the level of scrutiny with respect to privacy, security, regulatory compliance, is only increasing as well. Which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. So it's become much more acute. And to your earlier point, we do live in a different world, and the past couple of years, we could probably just kind of brute force it, right? We could focus on the top line. There was enough kind of investments to be had. I think nowadays organizations are focused, or are in a very different environment where there's much more focus on cost control, productivity, efficiency. How do we truly get value from that data? So again, I think it's just another incentive for organizations to now truly look at that data and to scale that data, not just from a technology and infrastructure perspective, but how do we actually scale data from an organizational perspective, right? Like you said, the people and process, how do we do that at scale? And that's only becoming much more important. And we do believe that the economic environment that we find ourselves in today is going to be a catalyst for organizations to really take that more seriously if you will than they maybe have in the past. >> You know, I don't know when you guys founded Collibra, if you had a sense as to how complicated it was going to get, but you've been on a mission to really address these problems from the beginning. How would you describe your mission, and what are you doing to address these challenges? >> Yeah, absolutely. We started Collibra in 2008. So in some sense in the last kind of financial crisis. And that was really the start of Collibra, where we found product market fit working with large financial institutions to help them cope with the increasing compliance requirements that they were faced with because of the financial crisis, and kind of here we are again in a very different environment of course, 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case, and across every source, frankly has only become more important. So while it's been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to, again, be able to provide everyone, and that's why we call it Data Citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy manner. That mission is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we're still relatively early in that journey. >> Well, that's interesting because, you know, in my observation, it takes seven to 10 years to actually build a company, and then the fact that you're still in the early days is kind of interesting. I mean, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your your current momentum? >> Yeah, absolutely. Again, there's a lot of tailwinds, organizations are only maturing their data practices, and we've seen it kind of transform, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world, whether it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the market with some of the cloud partners like Google, Amazon, Snowflake, Databricks, and others, right? As those kind of new modern data infrastructures, modern data architectures, are definitely all moving to the cloud. A great opportunity for us, our partners, and of course our customers, to help them kind of transition to the cloud even faster. And so we see a lot of excitement and momentum there. We did an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging AI, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical. So we're really excited about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believed in. Federated, focused on domains, giving a lot of ownership to different teams. I think that's the way to scale the data organizations, and so that aligns really well with our vision, and from a product perspective, we've seen a lot of momentum with our customers there as well. >> Yeah, you know, a couple things there. I mean, the acquisition of OwlDQ, you know, Kirk Haslbeck and their team, it's interesting, you know, the whole data quality used to be this back office function and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh, you mentioned. You guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're a critical part of many ecosystems, and you're developing your own ecosystem. So let's chat a little bit about the products. We're going to go deeper into products later on at Data Citizens '22, but we know you're debuting some new innovations, you know, whether it's, you know, the under the covers in security, sort of making data more accessible for people, just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >> Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme, and like I said, we're still relatively early in this journey towards kind of that mission of data intelligence, that really bold and compelling mission. Either customers are just starting on that journey, and we want to make it as easy as possible for the organization to actually get started, because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for, really to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving, a lot of kind of ease of adoption, ease of use, but also then, how do we make sure that as Collibra becomes this kind of mission critical enterprise platform from a security performance architecture scale, supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme. From an innovation perspective, from a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction. How do we make it easy? How do we make available a true kind of shopping experience so that anybody in your organization can, in a very easy search first way, find the right data product, find the right data set that data can then consume, use its analytics. How do we help organizations drive adoption, tell them where they're working really well, and where they have opportunities. Home pages, again, to make things easy for people, for anyone in your organization, to kind of get started with Collibra. You mentioned workflow designer, again, we have a very powerful enterprise platform. One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code, kind of workflow designer experience. So really customers can take it to the next level. There's a lot more new product around Collibra Protect, which in partnership with Snowflake, which has been a strategic investor in Collibra, focused on how do we make access governance easier? How do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data, is managed in a much more effective way. Really excited about that product. There's more around data quality. Again, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. So we launched our data quality cloud product as well as making use of those native compute capabilities in platforms like Snowflake, Databricks, Google, Amazon, and others. And so we are bettering a capability that we call push down. So we're actually pushing down the computer and data quality, the monitoring, into the underlying platform, which again, from a scale performance and ease of use perspective is going to make a massive difference. And then more broadly, we talked a little bit about the ecosystem. Again, integrations that we talk about, being able to connect to every source. Integrations are absolutely critical, and we're really excited to deliver new integrations with Snowflake, Azure, and Google Cloud Storage as well. So there's a lot coming out. The team has been at work really hard, and we are really, really excited about what we are coming, what we're bringing to markets. >> Yeah, a lot going on there. I wonder if you could give us your closing thoughts. I mean, you talked about the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles. You think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard, so how do you see sort of the future? And, you know, give us your closing thoughts please. >> Yeah, absolutely. And I think we're really at this pivotal moment, and I think you said it well. We all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not going to fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, the data intelligence platform. We are still early, making it as easy as we can. It's kind of our, as our mission. And so I'm really, really excited to see what we are going to, how the markets are going to evolve over the next few quarters and years. I think the trend is clearly there, when we talk about data mesh, this kind of federated approach, focus on data products is just another signal that we believe that a lot of our organizations are now at the time, they understand the need to go beyond just the technology, how to really, really think about how to actually scale data as a business function, just like we've done with IT, with HR, with sales and marketing, with finance. That's how we need to think about data. I think now's the time given the economic environment that we are in, much more focus on control, much more focus on productivity, efficiency, and now's the time we need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >> Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much, and good luck in San Diego. I know you're going to crush it out there. >> Thank you Dave. >> Yeah, it's a great spot for an in person event, and of course, the content post event is going to be available at collibra.com, and you can of course catch the Cube coverage at thecube.net, and all the news at siliconangle.com. This is Dave Vellante for the Cube, your leader in enterprise and emerging tech coverage. (light music)

>>Hi, Felix. Great to be here. >>Likewise. Um, so when I started reading about data mesh, I think about a year ago, I found myself the more I read about it, the more I find myself agreeing with other principles behind data mesh, it actually took me back to almost the starting of Colibra 13 years ago, based on the research we were doing on semantic technologies, even personally my own master thesis, which was about domain driven ontologies. And we'll talk about domain-driven as it's a key principle behind data mesh, but before we get into that, let's not assume that everybody knows what data measures about. Although we've seen a lot of traction and momentum, which is fantastic to see, but maybe if you could start by talking about some of the key principles and, and a brief overview of what data mesh, uh, Isabella of >>Course, well, they're happy to, uh, so Dana mesh is an approach is a new approach. It's a decentralized, decentralized approach to managing and accessing data and particularly analytical data at scale. So we can break that down a little bit. What is analytical data? Well, analytical data is the data that fuels our reporting as a business intelligence. Most importantly, the machine learning training, right? So it's the data, that's, it's an aggregate view of historical events that happens across organizations, many domains within organizations, or even beyond one organization, right? Um, and today we manage, uh, this analytical data through very centralized solutions. So whether it's a data lake or data warehouse or combinations of the two, and, uh, to be honest, we have kind of outsource the accountability for it, to the data team, right? It doesn't happen within the domains. Uh, what we have found ourselves with is, uh, central button next. >>So as we see the growth in the scale of organizations, in terms of the origins of the data and in terms of the great expectations for the data, all of these wonderful use cases that are, that requires access to that, unless we're data, uh, we find ourselves kind of constraints and limited in agility to respond, you know, because we have a centralized bottleneck from team to technology, to architecture. So there's a mesh kind of is that looks at the past what we've done, accidental complexity that we've kind of created and tries to reimagine a different way of, uh, managing and accessing data that can truly scale as this origins of the data grows. As they become available within one organization, we didn't want a cloud or another, and it links down really the approach based on four principles. Uh, so I so far, I haven't tried to be prescriptive as exactly how you implement it. >>I leave that to Elizabeth, to the imaginations of the users. Um, of course I have my opinions, but, but without being prescriptive, I think there are full shifts that needs to happen. One is, uh, we need to start breaking down the, kind of this complex problem of accessing to data around boundaries that can allow this to scale out a solution. So boundaries that are, that naturally fits into that model or domains, right. Our business domain. So, so there's a first principle is the domain ownership of the data. So analytical data will be shared and served and accountable, uh, by the domains where they come from. And then the second dimension of that is, okay. So once we break down this, the ownership of the database on domains, how can we prevent this data siloing? So the second principle is really treating data as a product. >>So considering the success of that data based on the access and usability and the lifelong experience of data analysts, data scientists. So we talk about data as a product and that the third principle is to really make it possible feasible. We need to really rethink our data platforms, our infrastructure capabilities, and create a new set ourselves of capabilities that allows domain in fact, to own their data in fact, to manage the life cycle of their analytical data. So then self-serve daytime frustration and platform is the fourth principle. And the last principle is really around governance because we have to think about governance. In fact, when I first wrote it down, this was like a little kind of concern in, in embedded in what some of my texts and I thought about, okay, now to make this real, we need to think about securing and quality of the data accessibility of the data at scale, in a fashion that embraces this autonomous domain ownership. So we have to think about how can we make this real with competition of governance? How can we make those domains be part of the governance, federated governance, federally, the competition of governance is the fourth principle. So at insurance it's a organizational shift, it's an architectural change. And of course technology needs to change to get us to decentralize access and management of Emily's school data. >>Yeah, I think that makes a ton of sense. If you want to scale, typically you have to think much more distributed versus centralized at we've seen it in other practices as well, that domain-driven thinking as well. I think, especially around engineering, right? We've seen a lot of the same principles and best practices in order to scale engineering teams and not make the same mistakes again, but maybe we can start there with kind of the core principles around that domain driven thinking. Can you elaborate a little bit on that? Why that is so important than the kind of data organizations, data functions as well? >>Absolutely. I mean, if you look at your organizations, organizations are complex systems, right? There are eight made of parts, which are basically domains functions of the business, your automation and your customer management, yourselves marketing. And then the behavior of the organization is the result of an intuitive, you know, network of dependencies and interactions with these domains. So if we just overlay data on this complex system, it does make sense to really, to scale, to bring the ownership and, um, really access to data right at the domain where it originates, right. But to the people who know that data best and most capable of providing that data. So to optimize response, to change, to optimize creating new features, new services, new machine learning models, we've got to kind of think about your call optimization, but not that the cost of global good. Right. Uh, so the domain ownership really talks about giving autonomy to the domains and accountability to provide their data and model the data, um, in a responsible way, be accountable for its quality. >>So no collect some of the empower them and localize some of those responsibilities, but at the same time, you know, thinking about the global goods, so what are they, how that domain needs to be accountable against the other domains on the mission? That's the governance piece covers that. And that leads to some interesting kind of architectural shifts, because when you think about not submission of the data, then you think about, okay, if I have a machine learning model that needs, you know, three pieces of the data from the different domains, I ended up actually distributing the computer also back to those domains. So it actually starts shifting kind of architectural as well. We start with ownership. Yeah, >>No, I think that makes a ton of sense, but I can imagine people thinking, well, if you're organizing, according to these domains, aren't gonna be going to grades different silos, even more silos. And I think that's where it second principle that's, um, think of data as a product and it comes in, I think that's incredibly powerful in my mind. It's powerful because it helps us think about usability. It helps us think about the consumer of that data and really packaging it in the right way. And as one sentence that I've heard you use that I think is incredibly powerful, it's less collecting, more connecting. Um, and can you elaborate on that a little bit? >>Absolutely. I mean the power and the value of the data is not enhanced, which we have got and stored on this, right. It's really about connecting that data to other data sets to aluminate new insights. The higher order information is connecting that data to the users, right. Then they want to use it. So that's why I think, uh, if we shift that thinking from just collecting more in one place, like whatever, and ability to connect datasets, then, then arrive at a different solution. So, uh, I think data as a product, as you said, exactly, was a kind of a response to the challenges that domain-driven siloing could create. And the idea is that the data that now these domains own needs to be shared with some accountability and incentive structure as a product. So if you bring product thinking to data, what does that mean? >>That means delighting the experience that there are users who are they, they're the data analysts, data scientists. So, you know, how can we delight their experience of their journey starts with a hypothesis. I have a question. Do I have right data to answer this question with a particular model? Let me discover it, let me find it if it's useful. Do I trust it? So really fascinated in that journey? I think we have two choices in that we have the choice of source of that data. The people who are really shouldn't be accountable for it, shrug off the responsibility and say, you know, I dumped this data on some event streaming and somebody downstream, the governance or data team will take care of a terror again. So it usable piece of information. And that's what we have done for, you know, half century almost. And, or let's say let's bring intention of providing quality data back to the source and make the folks both empower them and make them accountable for providing that data right at the source as a product. And I think by being intentional about that, um, w we're going to remove a lot of accidental complexity that we have created with, you know, labyrinth pipelines of moving data from one place to another, and try to build quality back into it. Um, and that requires, you know, architectural shifts, organizational shifts, incentive models, and the whole package, >>The hope is absolutely. And we'll talk about that. Federated computational governance is going to be a really an important aspect, but the other part of kind of data as a product next to usability is whole trust. Right? If you, if you want to use it, why is also trusts so important if you think about data as a product? >>Well, uh, I mean, maybe we turn this question back to you. Would you buy the shiniest product if you don't trust it, if you, if you don't trust where it comes from, can I use it? Is it, does it have integrity? I wouldn't. I think, I think it's almost irresponsible to use the data that you can trust, right. And the, really the meaning of the trust is that, do I know enough about this data to, to, for it, to be useful for the purpose that I'm using it for? So, um, I think trust is absolutely fundamental to, as a fundamental characteristics of a data as a product. And again, it comes back to breaching the gap between what the data user knows needs to know to really trust them, use that data, to find it, whether it's suitable and what they know today. So we can bridge that gap with, uh, you know, adding documentation, adding SLRs, adding lineage, like all of these additional information, but not only that, but also having people that are accountable for providing that integrity and those silos and guaranteeing. So it's really those product owners. So I think, um, it's just, for me, it's a non trust is a non-negotiable characteristic of the data as a product, like any other consumer product. >>Exactly. Like you said, if you think about consumer product, consumer marketplace is almost Uber of Amazon, of Airbnb. You have the simple rating as a very simple way of showing trust and those two and those different stakeholders and that almost. And we also say, okay, how do we actually get there? And I think data measure also talks a little bit about the roles responsibilities. And I think the importance overall of a, of a data product owner probably is aligned with that, that importance and trust. Yeah, >>Absolutely. I think we can't just wish for these good things happens without putting the accountability and the right roles in place. And the data product owner is just the starting point for us to stop playing hot potato. When it comes to, you know, who owns the data will be accountable for not so much. Who's the actual owner of that data because the owner of the data is you and me where the data comes really from, but it's the data product owner who's going to be responsible for the life cycle of this. They know when the data gets changed with consumers, meaning you feel as a new information, make sure that that gets carried out and maybe one day retire that data. So that long term ownership with intimate understanding of the needs of the user for that data, as well as the data itself and the domain itself and managing the life cycle of that, uh, I think that's a, that's a necessary role. >>Um, and then we have to think about why would anybody want to be a data product owner, right? What are the incentives we have to set up in the infrastructure, you know, in the organization. Um, and it really comes down to, I think, adopting prior art that exists in the product ownership landscape and bring it really to the data and assume the data users as the, as the customers, right. To make them happy. So our incentives on KPIs for these people before they get product on it needs to be aligned with the happiness of their data users. >>Yep. I love that. The alignment again, to the consumer using things like we know from product management, product owner of these roles and reusing that for data, I think that makes it makes a ton of sense. And it's a good leeway to talk a little about governance, right? We mentioned already federated governance, computational governance at we seeing that challenge often with our customers centralizing versus decentralizing. How do we find the right balance? Can you talk a little bit about that in the context of data mesh? How do we, how do we do this? >>Yeah, absolutely. I think the, I was hoping to pack three concepts in the title of the governance, but I thought that would be quite mouthful. So, uh, as you mentioned, uh, the kind of that federated aspects, the competition aspects, and I think embedded governance, I would, if I could add another kind of phrasing there and really it's about, um, as we talked about to how to make it happen. So I think the Federation matters because the people who are really in a position listed this, their product owners in a position to provide data in a trustworthy, with integrity and secure way, they have to have a stake in doing that, right. They have to be accountable, not just for their little domain or a big domain, but also they have to have an accountability for the mesh. So some of the concerns that are applied to all of the data front, I've seen fluid, how we secure them are consistently really secure them. >>How do we model the data or the schema language or the SLO metrics, or that allows this, uh, data to be interoperable so we can join multiple data products. So we have to have, I think, a set of policies that are really minimum set of policies that we have to apply globally to all the data products and then in a federated fashion, incentivize the data product owners. So have a stake in that and make that happen because there's always going to be a challenge in prioritizing. Would I add another few attributes? So my data sets to make my customers happy, or would I adopt that this standardized modeling language, right? They have to make that kind of continuous, um, kind of prioritization. Um, and they have to be incentivized to do both. Right. Uh, and then the other piece of it is okay, if we want to apply these consistent policies, across many data products and the mesh, how would it be physically possible? >>And the only way I can see, and I have seen it done in service mesh would be possible is by embedding those policies as competition, as code into every single data product. And how do we do that again, platform has a big part of it. So be able to have this embedded policy engines and whatever those things are into the data products, uh, and to, to be able to competition. So by default, when you become a data product, as part of the scaffolding of that data product, you get all of these, um, kind of computational capabilities to configure your, your policies according to the global policies. >>No, that makes sense. That makes, that makes it on a sense. That makes sense. >>I'm just curious. Really. So you've been at this for a while. You've built this system for the 13 years came from kind of academic background. So, uh, to be honest, we run into your products, lots of our clients, and there's always like a chat conversation within ThoughtWorks that, uh, do you guys know about this product then? So and so, oh, I should have curious, well, how do you think data governance tehcnology then skip and you need to shift with data mesh, right. And, and if, if I would ask, how would your roadmap changes with database? >>Yeah, I think it's a really good question. Um, what I don't want to do is to make, make the mistake that Venice often make and think of data mesh as a product. I think it's a much more holistic mindset change, right? That that's organization. Yes. It needs to be a kind of a platform enablement component there. And we've actually, I think authentically what, how we think about governance, that's very aligned with some of the principles and data measures that federate their thinking or customers know about going to communities domains or operating model. We really support that flexibility. I think from a roadmap perspective, I think making that even easier, uh, as always kind of a, a focus focus area for us, um, specifically around data measures are a few things that come to mind. Uh, one, I think is connectivity, right? If you, if you give different teams more ownership and accountability, we're not going to live in a world where all of the data is going to be stored on one location, right? >>You want to give people themes the opportunity and the accountability to make their own technology decisions so that they are fit for purpose. So I think whatever platform being able to really provide out of the box connectivity to a very wide, um, area or a range of technologies, I think is absolutely critical, um, on the, on the product as a or data as a product, thinking that usability, I think that's top of mind, uh, that's part of our roadmap. You're going to hear us, uh, stock about that tomorrow as well. Um, that data consumer, how do we make it as easy as possible for people to discover data that they can trust that they can access? Um, and in that thinking is a big part of our roadmap. So again, making that as easy as possible, uh, is a, is a big part of it. >>And, and also on the, I think the computation aspect that you mentioned, I think we believe in as well, if, if it's just documentation is going to be really hard to keep that alive, right? And so you have to make an active, we have to get close to the actual data. So if you think about a policy enforcement, for example, some things we're talking about, it's not just definition is the enforcement data quality. That's why we are so excited about our or data quality, um, acquisition as well. Um, so these are a couple of the things that we're thinking of, again, your, your, um, your, your, uh, message around from collecting to connecting. We talk about unity. I think that that works really, really well with our mission and vision as well. So mark, thank you so much. I wish we had more time to continue the conversation, uh, but it's been great to have a conversation here. Thank you so much for being here today and, uh, let's continue to work on that on data. Hello. I'm excited >>To see it. Just come to like.

>>At the beginning of the last decade, the technology industry was a buzzing because we were on the cusp of a new era of data. The promise of so-called big data was that it would enable data-driven organizations to tap a new form of competitive advantage. Namely insights from data at a much lower cost. The problem was data became plentiful, but insights. They remained scarce, a rash of technical complexity combined with a lack of trust due to conflicting data sources and inconsistent definitions led to the same story that we've heard for decades. We spent a ton of time and money to create a single version of the truth. And we're further away than we've ever been before. Maybe as an industry, we should be approaching this problem differently. Perhaps it should start with the idea that we have to change the way we serve business users. I E those who understand data context, and with me to discuss the evolving data space, his company, and the upcoming data citizens conference is Felix van de Mala, the CEO and founder of Collibra. Felix. Welcome. Great to see you. >>Great to see you. Great to be here. >>So tell us a little bit about Collibra and the problem that you're solving. Maybe you could double click on my upfront narrative. >>Yeah, I think you said it really well. Uh, we've seen so much innovation over the last couple of years in data, the exploding volume complexity of data. We've seen a lot of innovation of how to store and process that data, that, that volume of data more effectively or more cost-effectively, but fundamentally the source of the problem as being able to really derive insights from that data effectively when it's for an AI model or for reporting, it's still as difficult as it was, let's say 10 years ago. And if only in a way it's only become more, uh, more difficult. And so what we fundamentally believe is that next to that innovation on the infrastructure side of data, you really need to look at the people on process side of data. There's so many more people that today consume and produce data to do their job. >>That's why we talk about data citizens. They have to make it easier for them to find the right data in a way that they can trust that there's confidence in that data to be able to make decisions and to be able to trust the output of that, uh, of that model. And that's really what is focused on initially around governance. Uh, how do you make sure people actually are companies know what data they have and make sure they can trust it and they can use it in a compliant way. And now we've extended that into the only data intelligence platform today in the industry where we just make it easier for organizations to truly unite around the data across the whole organization, wherever that data is stored on premise and the cloud, whoever is actually using or consuming data. Uh, that's why we talk about data citizens. I >>Think you're right. I think it is more complex. There's just more of it. And there's more pressure on individuals to get advantage from it. But I, to ask you what sets Culebra apart, because I'd like you to explain why you're not just another data company chasing a problem with w it's going to be an incremental solution. It's really not going to change anything. What, what sets Collibra apart? >>Yeah, that's a really good question. And I think what's fundamentally sets us apart. What makes us unique is that we look at data or the problem around data as truly a business problem and a business function. So we fundamentally believe that if you believe that data is an asset, you really have to run it as a, as a, as a strategic business functions, just like your, um, uh, your HR function, your people function, your it functioning says a marketing function. You have a system to run that function. Now you have Salesforce to run sales and marketing. You have service now to run your, it function. You have Workday to run your people function, but you need the same system to really run your data from. And that's really how we think about GDPR. So we not another kind of faster, better database we know than other data management tool that makes the life of a single individual easier, which really a business application that focuses on how do we bring people together and effective rate so that they can collaborate around the data. It creates efficiency. So you don't have to do things ad hoc. You can easily find the right information. You can collaborate effectively. And it creates the confidence to actually be able to do something with the outcomes of it, the results of all of that work. And so fundamentally I'm looking at the problem as a, as a business function that needs a business system. We call it the system of record or system of engagement for the, for the data function, I think is absolutely critical and, and really unique in the, in our approach. So >>Data citizens are big user conference, data citizens, 21, it's coming up June 16th and 17th, the cubes stoked because we love talking about data. This is the first time we're bringing the cube to that event. So we're really gearing up for it. And I wonder if it could tell us a little bit about the history and the evolution of the data citizens conference? >>Absolutely. I think the first one is set at six years ago where we had a small event at a hotel downtown New York. Uh, most of the customers as their user conference, a lot of the banks, which are at the time of the main customers at 60 people. So very small events, and it exploded ever since, uh, this year we expect over 5,000 people. So it's really expanded beyond just the user conference to really become more of almost the community conference and the industry, um, the conference. So we're really excited, a big part of what we do, why we care so much about the conference. That's an opportunity to build that data citizens community. That's what we hear from our customers, from all attendees that come to the conference, uh, bring those people to get us all care about the same topic and are passionate about doing more at data, uh, being able to connect, uh, connect people together as a big part of that. So we've always, uh, we're always looking forwards, uh, through the event, uh, from that perspective >>Competition, of course, for virtual events these days with them, what's in it for me, what, who should attend and what can attendees expect from data citizens? 21. >>Yeah, absolutely. The good thing about the virtual event, uh, event is that everybody can attend. It's free, it's open from across the road, of course, but what we want for people to take away as attendees is that you learn something at pragmatics or the next day on the job, you can do something. You've learned something very specific. We've also been, um, um, excited and looked at what is possible from an innovation perspective. And so that's how we look at the events. We bring a lot of, um, uh, customers on my realization that they're going to share their best practices, very specifically, how they are, how they are handling data governance, how they're doing data, data, cataloging, how they're doing data privacy. So very specific best practices and tips on how to be successful, but then also industry experts that can paint the picture of where we going as an industry, what are the best practices? >>What do we need to think about today to be ready for what's going to come tomorrow? So that's a big focus. We, of course, we're going to talk about and our product. What are we, what do we have in store from a product roadmap and innovation perspective? How are we helping these organizations get their foster and not aspect as we were being in a lot of partners as well? Um, and so that's a big part of that broader ecosystem, uh, which is, which is really interesting. And I finally, like I said, it's really around the community, right? And that's what we hear continuously from the attendees. Just being able to make these connections, learn new people, learn what they're doing, how they've, uh, kind of, um, solved certain challenges. We hear that's a really big part of, uh, of the value proposition. So as an attendee, uh, the good thing is you can, you can join from anywhere. Uh, all of the content is going to be available on demand. So later it's going to be available for you to have to look at as well. Plus you're going to be farther out. You're going to become part of that data, citizens community, which has a really thriving and growing community where you're going to find a lot of like-minded people with the same passion, the same interest that McConnell learned the most from, well, I'd rather >>Like the term data citizen. I consider myself a data citizen, and it has implications just in terms of putting data in the hands of, of business users. So it's sort of central to this event, obviously. W what is a data citizen to Collibra? >>Yeah, it's, it's a really core part of our mission and our vision that we believe that today everyone needs data to do their job. Everyone in that sense has become a data citizen in the sense that they need to be able to easily access trustworthy data. We have to make it easy for people to easily find the right data that they can trust that they can understand. And I can do something like with and make their job easier. On the other hand, like a citizen, you have rights and you have responsibilities as a data citizen. You also have the responsibility to treat that data in the right way to make sure from a privacy and security perspective, that data is a as again, like I said, treated in the right way. And so that combination of making it easy, making it accessible, democratizing it, uh, but also making sure we treat data in the right way is really important. And that's a core part of what we believe that everyone is going to become a data citizen. And so, um, that's a big part of our mission. I like that >>We're to enter into a contract, I'll do my part and you'll give me access to that data. I think that's a great philosophy. So the call to action here, June 16th and 17th, go register@citizensdotcollibra.com go register because it's not just the normal mumbo jumbo. You're going to get some really interesting data. Felix, I'll give you the last word. >>No, like I said, it's like you said, go register. It's a great event. It's a great community to be part of June 16 at 17, you can block it in your calendar. So go to citizens up pretty bad outcome. It's going to be a, it's going to be a great event. Thanks for helping >>Us preview. Uh, this event is going to be a great event that really excited about Felix. Great to see you. And we'll see you on June 16th and 17th. Absolutely. All right. Thanks for watching everybody. This is Dave Volante for the cube. We'll see you next time.

(upbeat music) >> At the beginning of last decade technology industry was a buzzing because we were on the cusp of a new era of data. The promise of so-called big data was that it would enable data-driven organizations to tap a new form of competitive advantage. Namely insights from data at a much lower cost. The problem was data became plentiful, but insights, they remain scarce. A rash of technical complexity combined with a lack of trust due to conflicting data sources and inconsistent definitions led to the same story that we've heard for decades. We spent a ton of time and money to create a single version of the truth. And we're further away than we've ever been before. Maybe as an industry, we should be approaching this problem differently. Perhaps it should start with the idea that we have to change the way we serve business users i.e. those who understand data context. And with me, to discuss the evolving data space, his company and the upcoming Data Citizens Conference is Felix Van De Maele, the CEO and Founder, of Collibra. Felix, welcome. Great to see you. >> Great to see you. Great to be here. >> So tell us a little bit about Collibra and the problem that you're solving. Maybe you could double click on my upfront narrative. >> Yeah, I think you said it really well. We've seen so much innovation over the last couple of years in data, the exploding volume complexity of data. We've seen a lot of innovation of how to store and process that data, that volume of data more effectively, more cost-effectively. But fundamentally the source of the problem as being able to really derive insights from that data effectively when it's for an AI model or for reporting is still as difficult as it was let's say 10 years ago. And it only... In a way it's only become more difficult. And so what we fundamentally believe is that next to that innovation on the infrastructure side of data you really need to look at the people on process side of data. There are so many more people that today consume and produce data to do their job. That's why we talk about data citizens. They have to make it easier for them to find the right data in a way that they can trust that there's confidence in that data to be able to make decisions and to be able to trust the algorithm of that model. And that's really what Collibra is focused on. Initially, around governance. How do you make sure people actually or companies know what data they have and make sure they can trust it and they can use it in a compliant way. And now we've extended that into the only data intelligence platform today in the industry where we just make it easier for organizations to truly unite around the data across the whole organization. wherever that data stored on premise and the cloud whoever is actually using or consuming that data. That's why we talk about data citizens. >> I think you're right. I think yours is more complex. There's more of it. And there's more pressure on individuals to get advantage from it. But I want to ask you, what sets Collibra apart because I'd like you to explain why you're not just another data company chasing a problem with it's going to be an incremental solution, it's really not going to change anything. What sets Collibra apart? >> Yeah, that's a really good question. And what fundamentally sets us apart, or makes us unique is that we look at data or the problem around data as truly a business owner and a business function. So we fundamentally believe that if you believe that data is an asset, you really have to run it as a strategic business function. Just like you run your HR function, your people function, your IT function your sales and marketing function. You have a system to run that function. And you have Salesforce to run sales and marketing. You have service now to run your IT function. You have word day to run your people function. Like you need the same system to really run your data function. And that's really how we think about Collibra. So we're not another kind of faster better database. We're not another data management tool that makes the life of a single individual easier. We're truly a business application that focuses on how do we bring people together and effective rates so that they can collaborate around the data. It creates efficiency. So you don't have to do things ad hoc. You can easily find the right information. You can collaborate effectively. And it creates the confidence to actually be able to do something with the outcomes or with the results of all of that work. And so fundamentally, looking at the problem as a business function that needs a business system. We call it the system of record or system of engagement. For the data function, I think it's absolutely a critical and really unique in our approach. >> So Data Citizens your big user conference. Data Citizens '21 it's coming up June 16th and 17th cubes stoked because we love talking about data. This is the first time we're bringing theCUBE to that event. And so we're really gearing up for it. And I wonder if you can tell us a little bit about the history and the evolution of the Data Citizens conference? >> Absolutely. I think the first one it started six years ago where we had a small event at a hotel downtown New York mostly customers as their user conference, a lot of the banks, which are at the time are the main customers at 60 people. So very small events. And it's exploded ever since this year, we expect over 5,000 people. So it's really expanded beyond just a user conference to really become more of almost a community conference and an industry conference. So we're really excited. A big part of what we do, why we care so much about the conference. That's an opportunity to build that data citizens community. That's where we hear from our customers, from all attendees that come to the conference, bring those people together that all care about the same topic and are passionate about doing more with data, being able to connect people together as a big part of that. So we've always... We're always looking forward to event from that perspective. >> Well, a lot of competition of course, for virtual events these days with them. What's in it for me? Who should attend? And what can attendees expect from Data Citizens '21? >> Yeah, absolutely. The good thing about the virtual event is that everybody can attend. It's free, it's open from across the world, of course. But what we want for people to take away as attendees is that you learn something pragmatic. So the next day on the job, you can do something. You've learned something very specific. We've also been excited and looked at what is possible from an innovation perspective? And so that's how we look at the event. We bring a lot of customers and organization that are going to share their best practices. Very specifically, how they're handling data governance. How they're doing data cataloging. How they're doing data privacy. So very specific best practices and tips on how to be successful, but then also industry experts that can paint a picture of where we're going as an industry, what are the best practices? What do we need to think about today to be ready for what's going to come tomorrow? So that's a big focus. We, of course, we're going to talk about Collibra and our product. What do we have in store from a product roadmap. And innovation perspective, how we're helping these organizations get there faster and all that aspect as we bring in a lot of partners as well. And so that's a big part of that broader ecosystem which is really interesting. And I finally, like I said it's really around the community. That's what we hear continuously from the attendees. Just being able to make these connections, learn new people, learn what they're doing how they've kind of solved certain challenges. We hear that's a really big part of the value proposition. So as an attendee, the good thing is you can join from anywhere. All of the content is going to be available on demand. So later it's going to be available for you to have to look at as well. Plus you're going to be part, or you're going to become part of that data citizens community. Which is a really thriving and growing community where you're going to find a lot of like-minded people with the same passion, the same interest, that we can all learn a lot from. >> I rather like the term data citizen. I consider myself a data citizen and it has implications just in terms of putting data in the hands of business users. So it's just sort of central to this event, obviously. What is a data citizen to Collibra? >> Yeah. It's a really core part of our mission and our vision that we believe that today everyone needs data to do their job. Everyone in that sense has become a data citizen in the sense that they need to be able to easily access trustworthy data. We have to make it easy for people to easily find the right data that they can trust, that they can understand and they can do something with and make their job easier. On the other hand, like a citizen, you have rights and you have responsibilities. As a data citizen, you also have the responsibility to treat that data in the right way. To make sure from a privacy and security perspective, that data is as again like I said, treated in the right way. And so that combination of making it easy, making it accessible, democratizing it but also making sure we treat data in the right way is really important. And it's a core part of what we believe that everyone is going to become a data citizen. And so that's a big part of our mission. >> I like that. We're going to enter into a contract. I'll do my part and you'll give me access to that data. I think that's a great philosophy. So the call to action here, June 16th and 17th go register at citizens.collibra.com go register because it's not just the normal mumbo jumbo. You're going to get some really interesting data. Felix, I'll give you the last word. >> No, like I said, like you said, go register. It's a great event. It's a great community to be part of at June 16th and 17th you can block it in your calendar. So go to citizens.collibra.com. It's going to be a great event. >> Well, thanks for helping us preview this event. It's going to be a great event that we're really excited about. Felix, great to see you. And we'll see you on June 16th and 17th. >> Absolutely. >> All right. Thanks for watching everyone. This is Dave Vellante for theCUBE. We'll see you next time. (upbeat music)

>>Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions and they were largely confined to regulatory regulated industries that had to comply with public policy mandates. But as the cloud went mainstream, the tech giants showed us how valuable data could become and the value proposition for data quality and trust. It evolved from primarily a compliance driven issue to becoming a lynchpin of competitive advantage. But data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper specialized skills to develop data architectures and processes to serve the myriad data needs of organizations. >>And it resulted in a lot of frustration with data initiatives for most organizations that didn't have the resources of the cloud guys and the social media giants to really attack their data problems and turn data into gold. This is why today for example, this quite a bit of momentum to rethinking monolithic data architectures. You see, you hear about initiatives like data mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business Uni users, you hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that, but also how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. >>In other words, while it's enticing to experiment and run fast and loose with data initiatives kinda like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated. And intelligence governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is gonna use data that isn't trusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. >>Hello and welcome to the Cube's coverage of Data Citizens made possible by Calibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Ante and I'm one of the hosts of our program, which is running in parallel to data citizens. Now at the Cube we like to say we extract the signal from the noise, and over the, the next couple of days, we're gonna feature some of the themes from the keynote speakers at Data Citizens and we'll hear from several of the executives. Felix Von Dala, who is the co-founder and CEO of Collibra, will join us along with one of the other founders of Collibra, Stan Christians, who's gonna join my colleague Lisa Martin. I'm gonna also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the, the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Hasselbeck. >>He's the vice president of Data quality at Collibra. He's an amazingly smart dude who founded Owl dq, a company that he sold to Col to Collibra last year. Now many companies, they didn't make it through the Hado era, you know, they missed the industry waves and they became Driftwood. Collibra, on the other hand, has evolved its business. They've leveraged the cloud, expanded its product portfolio, and leaned in heavily to some major partnerships with cloud providers, as well as receiving a strategic investment from Snowflake earlier this year. So it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. >>Last year, the Cube Covered Data Citizens Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hado movement, we had data lakes, we'd spark the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of ai, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you know, more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights for data, trust the data, and begin to use that data in new ways, fueling data, products, monetization and insights data citizens 2022 is back and we're pleased to have Felix Van Dema, who is the founder and CEO of Collibra. He's on the cube or excited to have you, Felix. Good to see you again. >>Likewise Dave. Thanks for having me again. >>You bet. All right, we're gonna get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever and get current on what Collibra has been up to over the past year and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >>Yeah, absolutely. And, and I think you said it well, Dave, and and the intro that that rising complexity and fragmentation in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use has only gotten kinda more, more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well, which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. >>So it's become much more acute. And, and to your earlier point, we do live in a different world and and the the past couple of years we could probably just kind of brute for it, right? We could focus on, on the top line. There was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are, are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, How do we truly get value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale it data, not just from a a technology and infrastructure perspective, but how do you actually scale data from an organizational perspective, right? You said at the the people and process, how do we do that at scale? And that's only, only only becoming much more important. And we do believe that the, the economic environment that we find ourselves in today is gonna be catalyst for organizations to really dig out more seriously if, if, if, if you will, than they maybe have in the have in the best. >>You know, I don't know when you guys founded Collibra, if, if you had a sense as to how complicated it was gonna get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >>Yeah, absolutely. We, we started Colli in 2008. So in some sense and the, the last kind of financial crisis, and that was really the, the start of Colli where we found product market fit, working with large finance institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis and kind of here we are again in a very different environment, of course 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it data citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we are still relatively early in that, in that journey. >>Well, that's interesting because, you know, in my observation it takes seven to 10 years to actually build a company and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your, your current momentum? >>Yeah, absolutely. Again, there's, there's a lot of tail organizations that are only maturing the data practices and we've seen it kind of transform or, or, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world where it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in, in the, in the markets with some of the cloud partners like Google, Amazon, Snowflake, data bricks and, and others, right? As those kind of new modern data infrastructures, modern data architectures that are definitely all moving to the cloud, a great opportunity for us, our partners and of course our customers to help them kind of transition to the cloud even faster. >>And so we see a lot of excitement and momentum there within an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course, data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging ai, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architecture arch architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical so that they're really excited about about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believe. Then federated focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations. And so that aligns really well with our vision and, and from a product perspective, we've seen a lot of momentum with our customers there as well. >>Yeah, you know, a couple things there. I mean, the acquisition of i l dq, you know, Kirk Hasselbeck and, and their team, it's interesting, you know, the whole data quality used to be this back office function and, and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh. You mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So let's chat a little bit about the, the products. We're gonna go deeper in into products later on at, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the, the the under the covers in security, sort of making data more accessible for people just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >>Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission, either customers are still start, are just starting on that, on that journey. We wanna make it as easy as possible for the, for our organization to actually get started because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for really to, to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving. >>A lot of kind of ease of adoption, ease of use, but also then how do we make sure that lio becomes this kind of mission critical enterprise platform from a security performance architecture scale supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme from an innovation perspective, From a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction, how to make it easy. How do we make, how do we make available to true kind of shopping experience that anybody in your organization can, in a very easy search first way, find the right data product, find the right dataset, that data can then consume usage analytics. How do you, how do we help organizations drive adoption, tell them where they're working really well and where they have opportunities homepages again to, to make things easy for, for people, for anyone in your organization to kind of get started with ppia, you mentioned workflow designer, again, we have a very powerful enterprise platform. >>One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around K Bear Protect, which in partnership with Snowflake, which has been a strategic investor in kib, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data is managed as much more effective, effective rate, really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. >>So we launch more data quality cloud product as well as making use of those, those native compute capabilities in platforms like Snowflake, Data, Bricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down. So actually pushing down the computer and data quality, the monitoring into the underlying platform, which again, from a scale performance and ease of use perspective is gonna make a massive difference. And then more broadly, we, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So there's a lot coming out. The, the team has been work at work really hard and we are really, really excited about what we are coming, what we're bringing to markets. >>Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you, you talked about, you know, the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard. So how do you see sort of the future and, you know, give us the, your closing thoughts please? >>Yeah, absolutely. And I, and I think we we're really at this pivotal moment, and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not gonna fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can. It's kind of, of our, it's our mission. And so I'm really, really excited to see what we, what we are gonna, how the marks gonna evolve over the next, next few quarters and years. I think the trend is clearly there when we talk about data mesh, this kind of federated approach folks on data products is just another signal that we believe that a lot of our organization are now at the time. >>The understanding need to go beyond just the technology. I really, really think about how do we actually scale data as a business function, just like we've done with it, with, with hr, with, with sales and marketing, with finance. That's how we need to think about data. I think now is the time given the economic environment that we are in much more focus on control, much more focused on productivity efficiency and now's the time. We need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >>Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much and good luck in, in San Diego. I know you're gonna crush it out there. >>Thank you Dave. >>Yeah, it's a great spot for an in-person event and, and of course the content post event is gonna be available@collibra.com and you can of course catch the cube coverage@thecube.net and all the news@siliconangle.com. This is Dave Valante for the cube, your leader in enterprise and emerging tech coverage. >>Hi, I'm Jay from Collibra's Data Office. Today I want to talk to you about Collibra's data intelligence cloud. We often say Collibra is a single system of engagement for all of your data. Now, when I say data, I mean data in the broadest sense of the word, including reference and metadata. Think of metrics, reports, APIs, systems, policies, and even business processes that produce or consume data. Now, the beauty of this platform is that it ensures all of your users have an easy way to find, understand, trust, and access data. But how do you get started? Well, here are seven steps to help you get going. One, start with the data. What's data intelligence? Without data leverage the Collibra data catalog to automatically profile and classify your enterprise data wherever that data lives, databases, data lakes or data warehouses, whether on the cloud or on premise. >>Two, you'll then wanna organize the data and you'll do that with data communities. This can be by department, find a business or functional team, however your organization organizes work and accountability. And for that you'll establish community owners, communities, make it easy for people to navigate through the platform, find the data and will help create a sense of belonging for users. An important and related side note here, we find it's typical in many organizations that data is thought of is just an asset and IT and data offices are viewed as the owners of it and who are really the central teams performing analytics as a service provider to the enterprise. We believe data is more than an asset, it's a true product that can be converted to value. And that also means establishing business ownership of data where that strategy and ROI come together with subject matter expertise. >>Okay, three. Next, back to those communities there, the data owners should explain and define their data, not just the tables and columns, but also the related business terms, metrics and KPIs. These objects we call these assets are typically organized into business glossaries and data dictionaries. I definitely recommend starting with the topics that are most important to the business. Four, those steps that enable you and your users to have some fun with it. Linking everything together builds your knowledge graph and also known as a metadata graph by linking or relating these assets together. For example, a data set to a KPI to a report now enables your users to see what we call the lineage diagram that visualizes where the data in your dashboards actually came from and what the data means and who's responsible for it. Speaking of which, here's five. Leverage the calibra trusted business reporting solution on the marketplace, which comes with workflows for those owners to certify their reports, KPIs, and data sets. >>This helps them force their trust in their data. Six, easy to navigate dashboards or landing pages right in your platform for your company's business processes are the most effective way for everyone to better understand and take action on data. Here's a pro tip, use the dashboard design kit on the marketplace to help you build compelling dashboards. Finally, seven, promote the value of this to your users and be sure to schedule enablement office hours and new employee onboarding sessions to get folks excited about what you've built and implemented. Better yet, invite all of those community and data owners to these sessions so that they can show off the value that they've created. Those are my seven tips to get going with Collibra. I hope these have been useful. For more information, be sure to visit collibra.com. >>Welcome to the Cube's coverage of Data Citizens 2022 Collibra's customer event. My name is Dave Valante. With us is Kirk Hasselbeck, who's the vice president of Data Quality of Collibra Kirk, good to see you. Welcome. >>Thanks for having me, Dave. Excited to be here. >>You bet. Okay, we're gonna discuss data quality observability. It's a hot trend right now. You founded a data quality company, OWL dq, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >>Yeah, absolutely. It's, it's definitely exciting times for data quality, which you're right, has been around for a long time. So why now and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And, and while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as, as to why this is becoming so important now. And, and I guess you could kind of break this down simply and think about if Dave, you and I were gonna build, you know, a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, you know, what the ramifications could be, what, what those incidents would look like, or maybe better yet, we try to build a, a new trading algorithm with a crossover strategy where the 50 day crosses the, the 10 day average. >>And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, you know, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. You know, I, I bought a car not too long ago and my dad called and said, How many cylinders does it have? And I realized in that moment, you know, I might have failed him because, cause I didn't know. And, and I used to ask those types of questions about any lock brakes and cylinders and, and you know, if it's manual or, or automatic and, and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips, I, I really don't know that much about it. >>And, and that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the, the individuals loading and consuming all of this data for the company actually may not know that much about the data itself, and that's not even their job anymore. So we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >>You know, the other thing too about data quality, and for years we did the MIT CDO IQ event, we didn't do it last year, Covid messed everything up. But the observation I would make there thoughts is, is it data quality? Used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a, a risk to data as a, as an asset. And now as we say, we're gonna talk about observability. And so it's really become front and center just the whole quality issue because data's so fundamental, hasn't it? >>Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my, my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And, and that's kind of what's going on. There's, there's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before calibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is, is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, you know, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's, it's not ever going to be based on one or two domain experts anymore. >>So, So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they, are they cousins? What's your perspective on that? >>Yeah, it's, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the, the lingo is constantly moving is, you know, as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens, it's wrong and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. You know, everybody's talking about fresh data and stale data and, and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good in the bads. That was kind of your, your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data, but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >>So what's the Collibra angle on all this stuff made the acquisition, you got data quality observability coming together, you guys have a lot of expertise in, in this area, but you hear providence of data, you just talked about, you know, stale data, you know, the, the whole trend toward real time. How is Calibra approaching the problem and what's unique about your approach? >>Well, I think where we're fortunate is with our background, myself and team, we sort of lived this problem for a long time, you know, in, in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the, the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution, it's more advanced than some of the observation techniques that that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights, and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong, just show me the big picture, help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows, you can actually achieve total data governance at this point with the acquisition of what was a Lineage company years ago, and then my company Ldq now Collibra, Data quality Collibra may be the best positioned for total data governance and intelligence in the space. >>Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, you know, they just said, Oh, it's a glitch, you know, so they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you gotta announce new products, right? You're yearly event what's, what's new. Give us a sense as to what products are coming out, but specifically around data quality and observability. >>Absolutely. There's this, you know, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks is Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook in to these databases. And while we've always worked with the the same databases in the past, they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did your, my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? >>And with these native integrations that we're building and about to unveil, here's kind of a sneak peek for, for next week at Data Citizens. We're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration, you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress, cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >>So this is interesting because what you just described, you know, you mentioned Snowflake, you mentioned Google, Oh actually you mentioned yeah, data bricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool, but then Google's got the open data cloud. If you heard, you know, Google next and now data bricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm, I'm hearing to, to really understand the relationships between all those and have confidence across, you know, it's like Jak Dani, you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And, and, and that's what you're bringing to the table, Is that right? Did I get that right? >>Yeah, that's right. And it's, for us, it's, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now, we can send them the, the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network costs, zero egress cost, zero latency of time. And so when you were to log into Big Query tomorrow using our tool or like, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there, just like all of the major brands that you mentioned, but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And, and we think that this positions us to be the leader there. >>I love this example because, you know, Barry talks about, wow, the cloud guys are gonna own the world and, and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. All right, Kirk, give us your, your final thoughts and on on the trends that we've talked about and Data Citizens 22. >>Absolutely. Well, I think, you know, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there, they wanna know where everything is, where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're gonna see more one click solutions, more SAS based solutions and solutions that hopefully prove faster time to value on, on all of these modern cloud platforms. >>Excellent. All right, Kurt Hasselbeck, thanks so much for coming on the Cube and previewing Data Citizens 22. Appreciate it. >>Thanks for having me, Dave. >>You're welcome. Right, and thank you for watching. Keep it right there for more coverage from the Cube. Welcome to the Cube's virtual Coverage of Data Citizens 2022. My name is Dave Valante and I'm here with Laura Sellers, who's the Chief Product Officer at Collibra, the host of Data Citizens. Laura, welcome. Good to see you. >>Thank you. Nice to be here. >>Yeah, your keynote at Data Citizens this year focused on, you know, your mission to drive ease of use and scale. Now when I think about historically fast access to the right data at the right time in a form that's really easily consumable, it's been kind of challenging, especially for business users. Can can you explain to our audience why this matters so much and what's actually different today in the data ecosystem to make this a reality? >>Yeah, definitely. So I think what we really need and what I hear from customers every single day is that we need a new approach to data management and our product teams. What inspired me to come to Calibra a little bit a over a year ago was really the fact that they're very focused on bringing trusted data to more users across more sources for more use cases. And so as we look at what we're announcing with these innovations of ease of use and scale, it's really about making teams more productive in getting started with and the ability to manage data across the entire organization. So we've been very focused on richer experiences, a broader ecosystem of partners, as well as a platform that delivers performance, scale and security that our users and teams need and demand. So as we look at, Oh, go ahead. >>I was gonna say, you know, when I look back at like the last 10 years, it was all about getting the technology to work and it was just so complicated. But, but please carry on. I'd love to hear more about this. >>Yeah, I, I really, you know, Collibra is a system of engagement for data and we really are working on bringing that entire system of engagement to life for everyone to leverage here and now. So what we're announcing from our ease of use side of the world is first our data marketplace. This is the ability for all users to discover and access data quickly and easily shop for it, if you will. The next thing that we're also introducing is the new homepage. It's really about the ability to drive adoption and have users find data more quickly. And then the two more areas of the ease of use side of the world is our world of usage analytics. And one of the big pushes and passions we have at Collibra is to help with this data driven culture that all companies are trying to create. And also helping with data literacy, with something like usage analytics, it's really about driving adoption of the CLE platform, understanding what's working, who's accessing it, what's not. And then finally we're also introducing what's called workflow designer. And we love our workflows at Libra, it's a big differentiator to be able to automate business processes. The designer is really about a way for more people to be able to create those workflows, collaborate on those workflow flows, as well as people to be able to easily interact with them. So a lot of exciting things when it comes to ease of use to make it easier for all users to find data. >>Y yes, there's definitely a lot to unpack there. I I, you know, you mentioned this idea of, of of, of shopping for the data. That's interesting to me. Why this analogy, metaphor or analogy, I always get those confused. I let's go with analogy. Why is it so important to data consumers? >>I think when you look at the world of data, and I talked about this system of engagement, it's really about making it more accessible to the masses. And what users are used to is a shopping experience like your Amazon, if you will. And so having a consumer grade experience where users can quickly go in and find the data, trust that data, understand where the data's coming from, and then be able to quickly access it, is the idea of being able to shop for it, just making it as simple as possible and really speeding the time to value for any of the business analysts, data analysts out there. >>Yeah, I think when you, you, you see a lot of discussion about rethinking data architectures, putting data in the hands of the users and business people, decentralized data and of course that's awesome. I love that. But of course then you have to have self-service infrastructure and you have to have governance. And those are really challenging. And I think so many organizations, they're facing adoption challenges, you know, when it comes to enabling teams generally, especially domain experts to adopt new data technologies, you know, like the, the tech comes fast and furious. You got all these open source projects and get really confusing. Of course it risks security, governance and all that good stuff. You got all this jargon. So where do you see, you know, the friction in adopting new data technologies? What's your point of view and how can organizations overcome these challenges? >>You're, you're dead on. There's so much technology and there's so much to stay on top of, which is part of the friction, right? It's just being able to stay ahead of, of and understand all the technologies that are coming. You also look at as there's so many more sources of data and people are migrating data to the cloud and they're migrating to new sources. Where the friction comes is really that ability to understand where the data came from, where it's moving to, and then also to be able to put the access controls on top of it. So people are only getting access to the data that they should be getting access to. So one of the other things we're announcing with, with all of the innovations that are coming is what we're doing around performance and scale. So with all of the data movement, with all of the data that's out there, the first thing we're launching in the world of performance and scale is our world of data quality. >>It's something that Collibra has been working on for the past year and a half, but we're launching the ability to have data quality in the cloud. So it's currently an on-premise offering, but we'll now be able to carry that over into the cloud for us to manage that way. We're also introducing the ability to push down data quality into Snowflake. So this is, again, one of those challenges is making sure that that data that you have is d is is high quality as you move forward. And so really another, we're just reducing friction. You already have Snowflake stood up. It's not another machine for you to manage, it's just push down capabilities into Snowflake to be able to track that quality. Another thing that we're launching with that is what we call Collibra Protect. And this is that ability for users to be able to ingest metadata, understand where the PII data is, and then set policies up on top of it. So very quickly be able to set policies and have them enforced at the data level. So anybody in the organization is only getting access to the data they should have access to. >>Here's Topica data quality is interesting. It's something that I've followed for a number of years. It used to be a back office function, you know, and really confined only to highly regulated industries like financial services and healthcare and government. You know, you look back over a decade ago, you didn't have this worry about personal information, g gdpr, and, you know, California Consumer Privacy Act all becomes, becomes so much important. The cloud is really changed things in terms of performance and scale and of course partnering for, for, with Snowflake it's all about sharing data and monetization, anything but a back office function. So it was kind of smart that you guys were early on and of course attracting them and as a, as an investor as well was very strong validation. What can you tell us about the nature of the relationship with Snowflake and specifically inter interested in sort of joint engineering or, and product innovation efforts, you know, beyond the standard go to market stuff? >>Definitely. So you mentioned there were a strategic investor in Calibra about a year ago. A little less than that I guess. We've been working with them though for over a year really tightly with their product and engineering teams to make sure that Collibra is adding real value. Our unified platform is touching pieces of our unified platform or touching all pieces of Snowflake. And when I say that, what I mean is we're first, you know, able to ingest data with Snowflake, which, which has always existed. We're able to profile and classify that data we're announcing with Calibra Protect this week that you're now able to create those policies on top of Snowflake and have them enforce. So again, people can get more value out of their snowflake more quickly as far as time to value with, with our policies for all business users to be able to create. >>We're also announcing Snowflake Lineage 2.0. So this is the ability to take stored procedures in Snowflake and understand the lineage of where did the data come from, how was it transformed with within Snowflake as well as the data quality. Pushdown, as I mentioned, data quality, you brought it up. It is a new, it is a, a big industry push and you know, one of the things I think Gartner mentioned is people are losing up to $15 million without having great data quality. So this push down capability for Snowflake really is again, a big ease of use push for us at Collibra of that ability to, to push it into snowflake, take advantage of the data, the data source, and the engine that already lives there and get the right and make sure you have the right quality. >>I mean, the nice thing about Snowflake, if you play in the Snowflake sandbox, you, you, you, you can get sort of a, you know, high degree of confidence that the data sharing can be done in a safe way. Bringing, you know, Collibra into the, into the story allows me to have that data quality and, and that governance that I, that I need. You know, we've said many times on the cube that one of the notable differences in cloud this decade versus last decade, I mean ob there are obvious differences just in terms of scale and scope, but it's shaping up to be about the strength of the ecosystems. That's really a hallmark of these big cloud players. I mean they're, it's a key factor for innovating, accelerating product delivery, filling gaps in, in the hyperscale offerings cuz you got more stack, you know, mature stack capabilities and you know, it creates this flywheel momentum as we often say. But, so my question is, how do you work with the hyperscalers? Like whether it's AWS or Google, whomever, and what do you see as your role and what's the Collibra sweet spot? >>Yeah, definitely. So, you know, one of the things I mentioned early on is the broader ecosystem of partners is what it's all about. And so we have that strong partnership with Snowflake. We also are doing more with Google around, you know, GCP and kbra protect there, but also tighter data plex integration. So similar to what you've seen with our strategic moves around Snowflake and, and really covering the broad ecosystem of what Collibra can do on top of that data source. We're extending that to the world of Google as well and the world of data plex. We also have great partners in SI's Infosys is somebody we spoke with at the conference who's done a lot of great work with Levi's as they're really important to help people with their whole data strategy and driving that data driven culture and, and Collibra being the core of it. >>Hi Laura, we're gonna, we're gonna end it there, but I wonder if you could kind of put a bow on, you know, this year, the event your, your perspectives. So just give us your closing thoughts. >>Yeah, definitely. So I, I wanna say this is one of the biggest releases Collibra's ever had. Definitely the biggest one since I've been with the company a little over a year. We have all these great new product innovations coming to really drive the ease of use to make data more valuable for users everywhere and, and companies everywhere. And so it's all about everybody being able to easily find, understand, and trust and get access to that data going forward. >>Well congratulations on all the pro progress. It was great to have you on the cube first time I believe, and really appreciate you, you taking the time with us. >>Yes, thank you for your time. >>You're very welcome. Okay, you're watching the coverage of Data Citizens 2022 on the cube, your leader in enterprise and emerging tech coverage. >>So data modernization oftentimes means moving some of your storage and computer to the cloud where you get the benefit of scale and security and so on. But ultimately it doesn't take away the silos that you have. We have more locations, more tools and more processes with which we try to get value from this data. To do that at scale in an organization, people involved in this process, they have to understand each other. So you need to unite those people across those tools, processes, and systems with a shared language. When I say customer, do you understand the same thing as you hearing customer? Are we counting them in the same way so that shared language unites us and that gives the opportunity for the organization as a whole to get the maximum value out of their data assets and then they can democratize data so everyone can properly use that shared language to find, understand, and trust the data asset that's available. >>And that's where Collibra comes in. We provide a centralized system of engagement that works across all of those locations and combines all of those different user types across the whole business. At Collibra, we say United by data and that also means that we're united by data with our customers. So here is some data about some of our customers. There was the case of an online do it yourself platform who grew their revenue almost three times from a marketing campaign that provided the right product in the right hands of the right people. In other case that comes to mind is from a financial services organization who saved over 800 K every year because they were able to reuse the same data in different kinds of reports and before there was spread out over different tools and processes and silos, and now the platform brought them together so they realized, oh, we're actually using the same data, let's find a way to make this more efficient. And the last example that comes to mind is that of a large home loan, home mortgage, mortgage loan provider where they have a very complex landscape, a very complex architecture legacy in the cloud, et cetera. And they're using our software, they're using our platform to unite all the people and those processes and tools to get a common view of data to manage their compliance at scale. >>Hey everyone, I'm Lisa Martin covering Data Citizens 22, brought to you by Collibra. This next conversation is gonna focus on the importance of data culture. One of our Cube alumni is back, Stan Christians is Collibra's co-founder and it's Chief Data citizens. Stan, it's great to have you back on the cube. >>Hey Lisa, nice to be. >>So we're gonna be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really re requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. >>Right. So as you know, our event is called Data Citizens because we believe that in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organizations, you have a lot of people, most of the employees in an organization are somehow gonna to be a data citizen, right? So you need to make sure that these people are aware of it. You need that. People have skills and competencies to do with data what necessary and that's on, all right? So what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss, we need to make this decision that your boss is also open to and able to interpret, you know, the data presented in dashboard to actually make that decision and take that action. Right? >>And once you have that why to the organization, that's when you have a good data culture. Now that's continuous effort for most organizations because they're always moving, somehow they're hiring new people and it has to be continuous effort because we've seen that on the hand. Organizations continue challenged their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other set hand of the equation, you have the benefit. You know, you might look at regulatory drivers like, we have to do this, right? But it's, it's much better right now to consider the competitive drivers, for example, and we did an IDC study earlier this year, quite interesting. I can recommend anyone to it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity. >>So the, the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, don't have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, ok, I'm doing this, you know, data culture for everyone, awakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this re reasons you're trying to bring both of those together and the ones that get data intelligence right, are successful and competitive. That's, and that's what we're seeing out there in the market. >>Absolutely. We know that just generally stand right, the organizations that are, are really creating a, a data culture and enabling everybody within the organization to become data citizens are, We know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Collibra advises customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. >>Of course, of course it's difficult for an organization to adapt but it's also necessary, as you just said, imagine that, you know, you're a modern day organization, laptops, what have you, you're not using those, right? Or you know, you're delivering them throughout organization, but not enabling your colleagues to actually do something with that asset. Same thing as through with data today, right? If you're not properly using the data asset and competitors are, they're gonna to get more advantage. So as to how you get this done, establish this. There's angles to look at, Lisa. So one angle is obviously the leadership whereby whoever is the boss of data in the organization, you typically have multiple bosses there, like achieve data officers. Sometimes there's, there's multiple, but they may have a different title, right? So I'm just gonna summarize it as a data leader for a second. >>So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data. And that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now that's one part because then you can leadership in the organization and also the business value. And that's important. Cause those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that right, is it's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You, I really have to win them over. And if you have those two combined and obviously a good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like s then the in place to really start upgrading that culture inch by inch if you'll, >>Yes, I like that. The recipe for success. So you are the co-founder of Collibra. You've worn many different hats along this journey. Now you're building Collibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Collibra and what maybe some of the specific projects are that Collibra's data office is working on. >>Yes, and it is indeed data citizens. There are a ton of speaks here, are very excited. You know, we have Barb from m MIT speaking about data monetization. We have Dilla at the last minute. So really exciting agen agenda. Can't wait to get back out there essentially. So over the years at, we've doing this since two and eight, so a good years and I think we have another decade of work ahead in the market, just to be very clear. Data is here to stick around as are we. And myself, you know, when you start a company, we were for people in a, if you, so everybody's wearing all sorts of hat at time. But over the years I've run, you know, presales that sales partnerships, product cetera. And as our company got a little bit biggish, we're now thousand two. Something like people in the company. >>I believe systems and processes become a lot important. So we said you CBRA isn't the size our customers we're getting there in of organization structure, process systems, et cetera. So we said it's really time for us to put our money where is and to our own data office, which is what we were seeing customers', organizations worldwide. And they organizations have HR units, they have a finance unit and over time they'll all have a department if you'll, that is responsible somehow for the data. So we said, ok, let's try to set an examples that other people can take away with it, right? Can take away from it. So we set up a data strategy, we started building data products, took care of the data infrastructure. That's sort of good stuff. And in doing all of that, ISA exactly as you said, we said, okay, we need to also use our product and our own practices and from that use, learn how we can make the product better, learn how we make, can make the practice better and share that learning with all the, and on, on the Monday mornings, we sometimes refer to eating our dog foods on Friday evenings. >>We referred to that drinking our own champagne. I like it. So we, we had a, we had the driver to do this. You know, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now how, how do we organize this? We have three pillars, and by no means is this a template that everyone should, this is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science. The data product builders, if you'll or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow and you know, the quality can be checked. >>And then we have a data intelligence or data governance builders where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the CBRA approach, which is, well, what are the challenges that our business stakeholders have in hr, finance, sales, marketing all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a map and started execution use of the use case. And a important ones are very simple. We them with our, our customers as well, people talking about the cata, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and privacy. So they have their process registry and they can see how the data flows. >>So that's a starting place and that turns into a marketplace so that if new analysts and data citizens join kbra, they immediately have a place to go to, to look at, see, ok, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access data. And another one that we is around trusted business. We're seeing that since, you know, self-service BI allowed everyone to make beautiful dashboards, you know, pie, pie charts. I always, my pet pee is the pie chart because I love buy and you shouldn't always be using pie charts. But essentially there's become proliferation of those reports. And now executives don't really know, okay, should I trust this report or that report the reporting on the same thing. But the numbers seem different, right? So that's why we have trusted this reporting. So we know if a, the dashboard, a data product essentially is built, we not that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either, Right. And that silver browser, right? Absolutely >>Decay. >>Exactly. Yes, >>Absolutely. Talk a little bit about some of the, the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? >>KPIs and measuring is a big topic in the, in the data chief data officer profession, I would say, and again, it always varies with to your organization, but there's a few that we use that might be of interest. Use those pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is gonna be more related to that uptime, right? Are the, is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption's a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data sciences and products. Are people using them? Are they getting value from it? >>Can we calculate that value in ay perspective, right? Yeah. So that we can to the rest of the business continue to say we're tracking all those numbers and those numbers indicate that value is generated and how much value estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in a data mesh. People talk about being the owner of a data domain, for example, like product or, or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open closed? How many data products are built according to process? And so and so forth. So these are an set of examples of, of KPIs. There's a, there's a lot more, but hopefully those can already inspire the audience. >>Absolutely. So we've, we've talked about the rise cheap data offices, it's only accelerating. You mentioned this is like a 10 year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? >>So we, we've seen indeed the, the role sort of grow up, I think in, in thousand 10 there may have been like 10 achieve data officers or something. Gartner has exact numbers on them, but then they grew, you know, industries and the number is estimated to be about 20,000 right now. Wow. And they evolved in a sort of stack of competencies, defensive data strategy, because the first chief data officers were more regulatory driven, offensive data strategy support for the digital program. And now all about data products, right? So as a data leader, you now need all of those competences and need to include them in, in your strategy. >>How is that going to evolve for the next couple of years? I wish I had one of those balls, right? But essentially I think for the next couple of years there's gonna be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the chief data. So you'll see over the years that's gonna evolve more digital and more data products. So for next years, my, my prediction is it's all products because it's an immediate link between data and, and the essentially, right? Right. So that's gonna be important and quite likely a new, some new things will be added on, which nobody can predict yet. But we'll see those pop up in a few years. I think there's gonna be a continued challenge for the chief officer role to become a real executive role as opposed to, you know, somebody who claims that they're executive, but then they're not, right? >>So the real reporting level into the board, into the CEO for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful and the ones who get that will the ones that do it on the basis of data monetization, right? Connecting value to the data and making that value clear to all the data citizens in the organization, right? And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned of course. And they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization and you make everyone in the organization think about data as an asset. >>Absolutely. Because there's so much value that can be extracted. Organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective. From the data citizen perspective. And as the data show that you mentioned in that IDC study, you mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the cube at Data Citizens 22. We appreciate it. >>Thanks for having me over >>From Data Citizens 22, I'm Lisa Martin, you're watching The Cube, the leader in live tech coverage. >>Okay, this concludes our coverage of Data Citizens 2022, brought to you by Collibra. Remember, all these videos are available on demand@thecube.net. And don't forget to check out silicon angle.com for all the news and wiki bod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner ETR Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to collibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on the Cube, your leader in enterprise and emerging tech coverage. We'll see you soon.

>>Really excited about this year's data, citizens with so many of you together. Uh, I'm going to talk today about accessible data, because what good is the data. If you can get it into your hands and shop for it, but you can't understand it. Uh, and I'm here today with, uh, bald, really thrilled to be here with Paul. Paul is an award-winning author on all topics data. I think 20 books with 21st on the way over 300 articles, he's been a frequent speaker. He's an expert in future trends. Uh, he's a VP at cognitive systems, uh, over at IBM teachers' data also, um, at the business school and as a champion of diversity initiatives. Paul, thank you for being here, really the conformance, uh, to the session with you. >>Oh, thanks for having me. It's a privilege. >>So let's get started with, uh, our origins and data poll. Um, and I'll start with a little story of my own. So, uh, I trained as an engineer way back when, uh, and, um, in one of the courses we got as an engineer, it was about databases. So we got the stick thick book of CQL and me being in it for the programming. I was like, well, who needs this stuff? And, uh, I wanted to do my part in terms of making data accessible. So essentially I, I was the only book that I sold on. Uh, obviously I learned some hard lessons, uh, later on, as I did a master's in AI after that, and then joined the database research lab at the university that Libra spun off from. Uh, but Hey, we all learned along the way. And, uh, Paula, I'm really curious. Um, when did you awaken first to data? If you will? >>You know, it's really interesting Stan, because I come from the opposite side, an undergrad in economics, uh, with some, uh, information systems research at the higher level. And so I think I was always attuned to what data could do, but I didn't understand how to get at it and the kinds of nuances around it. So then I started this job, a database company, like 27 years ago, and it started there, but I would say the awakening has never stopped because the data game is always changing. Like I look at these epochs that I've been through data. I was a real relational databases thinking third normal form, and then no SQL databases. And then I watch no SQL be about no don't use SQL, then wait a minute. Not only sequel. And today it's really for the data citizens about wait, no, I need SQL. So, um, I think I'm always waking up in data, so I'll call it a continuum if you will. But that was it. It was trying to figure out the technology behind driving analytics in which I took in school. >>Excellent. And I fully agree with you there. Uh, every couple of years they seem to reinvent new stuff and they want to be able to know SQL models. Let me see. I saw those come and go. Uh, obviously, and I think that's, that's a challenge for most people because in a way, data is a very abstract concepts, um, until you get down in the weeds and then it starts to become really, really messy, uh, until you, you know, from that end button extract a certain insights. Um, and as the next thing I want to talk about with you is that challenging organizations, we're hearing a lot about data, being valuable data, being the new oil data, being the new soil, the new gold, uh, data as an asset is being used as a slogan all over. Uh, people are investing a lot in data over multiple decades. Now there's a lot of new data technologies, always, but still, it seems that organizations fundamentally struggle with getting people access to data. What do you think are some of the key challenges that are underlying the struggles that mud, that organizations seem to face when it comes to data? >>Yeah. Listen, Stan, I'll tell you a lot of people I think are stuck on what I call their data, acumen curves, and you know, data is like a gym membership. If you don't use it, you're not going to get any value on it. And that's what I mean by accurate. And so I like to think that you use the analogy of some mud. There's like three layers that are holding a lot of organizations back at first is just the amount of data. Now, I'm not going to give you some stat about how many times I can go to the moon and back with the data regenerate, but I will give you one. I found interesting stat. The average human being in their lifetime will generate a petabyte of data. How much data is that? If that was my apple music playlist, it would be about 2000 years of nonstop music. >>So that's some kind of playlist. And I think what's happening for the first layer of mud is when I first started writing about data warehousing and analytics, I would be like, go find a needle in the haystack. But now it's really finding a needle in a stack of needles. So much data. So little time that's level one of mine. I think the second thing is people are looking for some kind of magic solution, like Cinderella's glass slipper, and you put it on her. She turns into a princess that's for Disney movies, right? And there's nothing magical about it. It is about skill and acumen and up-skilling. And I think if you're familiar with the duper, you recall the Hadoop craze, that's exactly what happened, right? Like people brought all their data together and everyone was going to be able to access it and give insights. >>And it teams said it was pretty successful, but every line of business I ever talked to said it was a complete failure. And the third layer is governance. That's actually where you're going to find some magic. And the problem in governance is every client I talked to is all about least effort to comply. They don't want to violate GDPR or California consumer protection act or whatever governance overlooks, where they do business and governance. When you don't lead me separate to comply and try not to get fine, but as an accelerant to your analytics, and that gets you out of that third layer of mud. So you start to invoke what I call the wisdom of the crowd. Now imagine taking all these different people with intelligence about the business and giving them access and acumen to hypothesize on thousands of ideas that turn into hundreds, we test and maybe dozens that go to production. So those are three layers that I think every organization is facing. >>Well. Um, I definitely follow on all the days, especially the one where people see governance as a, oh, I have to comply to this, which always hurts me a little bit, honestly, because all good governance is about making things easier while also making sure that they're less riskier. Um, but I do want to touch on that Hadoop thing a little bit, uh, because for me in my a decade or more over at Libra, we saw it come as well as go, let's say around 2015 to 2020 issue. So, and it's still around. Obviously once you put your data in something, it's very hard to make it go away, but I've always felt that had do, you know, it seemed like, oh, now we have a bunch of clusters and a bunch of network engineers. So what, >>Yeah. You know, Stan, I fell for, I wrote the book to do for dummies and it had such great promise. I think the problem is there wasn't enough education on how to extract value out of it. And that's why I say it thinks it's great. They liked clusters and engineers that you just said, but it didn't drive lineup >>Business. Got it. So do you think that the whole paradigm with the clouds that we're now on is going to fundamentally change that or is just an architectural change? >>Yeah. You know, it's, it's a great comment. What you're seeing today now is the movement for the data lake. Maybe a way from repositories, like Hadoop into cloud object stores, right? And then you look at CQL or other interfaces over that not allows me to really scale compute and storage separately, but that's all the technical stuff at the end of the day, whether you're on premise hybrid cloud, into cloud software, as a service, if you don't have the acumen for your entire organization to know how to work with data, get value from data, this whole data citizen thing. Um, you're not going to get the kind of value that goes into your investment, right? And I think that's the key thing that business leaders need to understand is it's not about analytics for kind of science project sakes. It's about analytics to drive. >>Absolutely. We fully agree with that. And I want to touch on that point. You mentioned about the wisdom of the crowds, the concept that I love about, right, and your organization is a big grout full of what we call data citizens. Now, if I remember correctly from the book of the wisdom of the crowds, there's, there's two points that really, you have to take Canada. What is, uh, for the wisdom of the grounds to work, you have to have all the individuals enabled, uh, for them to have access to the right information and to be able to share that information safely kept from the bias from others. Otherwise you're just biasing the outcome. And second, you need to be able to somehow aggregate that wisdom up to a certain decision. Uh, so as Felix mentioned earlier, we all are United by data and it's a data citizen topic. >>I want to touch on with you a little bit, because at Collibra we look at it as anyone who uses data to do their job, right. And 2020 has sort of accelerated digitization. Uh, but apart from that, I've always believed that, uh, you don't have to have data in your title, like a data analyst or a data scientist to be a data citizen. If I take a look at the example inside of Libra, we have product managers and they're trying to figure out which features are most important and how are they used and what patterns of behavior is there. You have a gal managers, and they're always trying to know the most they can about their specific accounts, uh, to be able to serve as them best. So for me, the data citizen is really in its broadest sense. Uh, anyone who uses data to do their job, does that, does that resonate with you? >>Yeah, absolutely. It reminds me of myself. And to be honest in my eyes where I got started from, and I agree, you don't need the word data in your title. What you need to have is curiosity, and that is in your culture and in your being. And, and I think as we look at organizations to transform and take full advantage of their, their data investments, they're going to need great governance. I guarantee you that, but then you're going to have to invest in this data citizen concept. And the first thing I'll tell you is, you know, that kind of acumen, if you will, as a team sport, it's not a departmental sport. So you need to think about what are the upskilling programs of where we can reach across to the technical and the non-technical, you know, lots and lots of businesses rely on Microsoft Excel. >>You have data citizens right there, but then there's other folks who are just flat out curious about stuff. And so now you have to open this up and invest in those people. Like, why are you paying people to think about your business without giving the data? It would be like hiring Tom Brady as a quarterback and telling him not to throw a pass. Right. And I see it all the time. So we kind of limit what we define as data citizen. And that's why I love what you said. You don't need the word data in your title and more so if you don't build the acumen, you don't know how to bring the data together, maybe how to wrangle it, but where did it come from? And where can you fixings? One company I worked with had 17 definitions for a sales individual, 17 definitions, and the talent team and HR couldn't drive to a single definition because they didn't have the data accurate. So when you start thinking of the data citizen, concept it about enabling everybody to shop for data much. Like I would look for a USB cable on Amazon, but also to attach to a business glossary for definition. So we have a common version of what a word means, the lineage of the data who owns it, who did it come from? What did it do? So bring that all together. And, uh, I will tell you companies that invest in the data, citizen concept, outperform companies that don't >>For all of that, I definitely fully agree that there's enough research out there that shows that the ones who are data-driven are capturing the most markets, but also capturing the most growth. So they're capturing the market even faster. And I love what you said, Paul, about, um, uh, the brains, right? You've already paid for the brains you've already invested in. So you may as well leverage them. Um, you may as well recognize and, and enable the data citizens, uh, to get access to the assets that they need to really do their job properly. That's what I want to touch on just a little bit, if, if you're capable, because for me, okay. Getting access to data is one thing, right? And I think you already touched on a few items there, but I'm shopping for data. Now I have it. I have a cul results set in my hands. Let's say, but I'm unable to read and write data. Right? I don't know how to analyze it. I don't know maybe about bias. Uh, maybe I, I, I don't know how to best visualize it. And maybe if I do, maybe I don't know how to craft a compelling persuasion narrative around it to change my bosses decisions. So from your viewpoint, do you think that it's wise for companies to continuously invest in data literacy to continuously upgrade that data citizens? If you will. >>Yeah, absolutely. Forest. I'm going to tell you right now, data literacy years are like dog years stage. So fast, new data types, new sources of data, new ways to get data like API APIs and microservices. But let me take it away from the technical concept for a bit. I want to talk to you about the movie. A star is born. I'm sure most of you have seen it or heard it Bradley Cooper, lady Gaga. So everyone knows the movie. What most people probably don't know is when lady Gaga teamed up with Bradley Cooper to do this movie, she demanded that he sing everything like nothing could be auto-tuned everything line. This is one of the leading actors of Hollywood. They filmed this remake in 42 days and Bradley Cooper spent 18 months on singing lessons. 18 months on a guitar lessons had a voice coach and it's so much and so forth. >>And so I think here's the point. If one of the best actors in the world has to invest three and a half years for 42 days to hit a movie out of the park. Why do we think we don't need a continuous investment in data literacy? Even once you've done your initial training, if you will, over the data, citizen, things are going to change. I don't, you don't. If I, you Stan, if you go to the gym and workout every day for three months, you'll never have to work out for the rest of your life. You would tell me I was ridiculous. So your data literacy is no different. And I will tell you, I have managed thousands of individuals, some of the most technical people around distinguished engineers, fellows, and data literacy comes from curiosity and a culture of never ending learning. That is the number one thing to success. >>And that curiosity, I hire people who are curious, I'll give you one more story. It's about Mozart. And this 21 year old comes to Mozart and he says, Mozart, can you teach me how to compose a symphony? And Mozart looks at this person that says, no, no, you're too young, too young. You compose your fourth symphony when you were 12 and Mozart looks at him and says, yeah, but I didn't go around asking people how to compose a symphony. Right? And so the notion of that story is curiosity. And those people who show up in always want to learn, they're your home run individuals. And they will bring data literacy across the organization. >>I love it. And I'm not going to try and be Mozart, but you know, three and a half years, I think you said two times, 18 months, uh, maybe there's hope for me yet in a singing, you'll be a good singer. Um, Duchy on the, on the, some of the sports references you've made, uh, Paul McGuire, we first connected, uh, I'm not gonna like disclose where you're from, but, uh, I saw he did come up and I know it all sorts of sports that drive to measure everything they can right on the field of the field. So let's imagine that you've done the best analysis, right? You're the most advanced data scientists schooled in the classics, as well as the modernist methods, the best tools you've made a beautiful analysis, beautiful dashboards. And now your coach just wants to put their favorite player on the game, despite what you're building to them. How do you deal with that kind of coaches? >>Yeah. Listen, this is a great question. I think for your data analytics strategy, but also for anyone listening and watching, who wants to just figure out how to drive a career forward? I would give the same advice. So the story you're talking about, indeed hockey, you can figure out where I'm from, but it's around the Ottawa senators, general manager. And he made a quote in an interview and he said, sometimes I want to punch my analytics, people in the head. Now I'm going to tell you, that's not a good culture for analytics. And he goes on to say, they tell me not to play this one player. This one player is very tough. You know, throws four or five hits a game. And he goes, I'd love my analytics people to get hit by bore a wacky and tell me how it feels. That's the player. >>Sure. I'm sure he hits hard, but here's the deal. When he's on the ice, the opposing team gets more shots on goal than the senators do on the opposing team. They score more goals, they lose. And so I think whenever you're trying to convince a movement forward, be it management, be it a project you're trying to fund. I always try to teach something that someone didn't previously know before and make them think, well, I never thought of it that way before. And I think the great opportunity right now, if you're trying to get moving in a data analytics strategy is around this post COVID era. You know, we've seen post COVID now really accelerate, or at least post COVID in certain parts of the world, but accelerate the appetite for digital transformation by about half a decade. Okay. And getting the data within your systems, as you digitize will give you all kinds of types of projects to make people think differently than the way they thought before. >>About data. I call this data exhaust. I'll give you a great example, Uber. I think we're all familiar with Uber. If we all remember back in the days when Uber would offer you search pricing. Okay? So basically you put Uber on your phone, they know everything about you, right? Who are your friends, where you going, uh, even how much batteries on your phone? Well, in a data science paper, I read a long time ago. They recognize that there was a 70% chance that you would accept a surge price. If you had less than 10% of your battery. So 10% of battery on your phone is an example of data exhaust all the lawns that you generate on your digital front end properties. Those are logs. You can take those together and maybe show executive management with data. We can understand why people abandoned their cart at the shipping phase, or what is the amount of shipping, which they abandoned it. When is the signal when our systems are about to go to go down. So, uh, I think that's a tremendous way. And if you look back to the sports, I mean the Atlanta Falcons NFL team, and they monitor their athletes, sleep performance, the Toronto Raptors basketball, they're running AI analytics on people's personalities and everything they tweet and every interview to see if the personality fits. So in sports, I think athletes are the most important commodity, if you will, or asset a yet all these teams are investing in analytics. So I think that's pretty telling, >>Okay, Paul, it looks like we're almost out of time. So in 30 seconds or less, what would you recommend to the data citizens out there? >>Okay. I'm going to give you a four tips in 30 seconds. Number one, remember learning never ends be curious forever. You'll drive your career. Number two, remember companies that invest in analytics and data, citizens outperform those that don't McKinsey says it's about 1.4 times across many KPIs. Number three, stop just collecting the dots and start connecting them with that. You need a strong governance strategy and that's going to help you for the future because the biggest thing in the future is not going to be about analytics, accuracy. It's going to be about analytics, explainability. So accuracy is no longer going to be enough. You're going to have to explain your decisions and finally stay positive and forever test negative. >>Love it. Thank you very much fall. Um, and for all the data seasons is out there. Um, when it comes down to access to data, it's more than just getting your hands on the data. It's also knowing what you can do with it, how you can do that and what you definitely shouldn't be doing with it. Uh, thank you everyone out there and enjoy your learning and interaction with the community. Stay healthy. Bye-bye.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome to the Virtual Cube and our coverage of aws reinvent 2020. I'm Lisa Martin. I'm joined by Ali Siddiqui, the chief product officer of BMC Software. We're gonna be talking about what BMC and A W s are doing together. Ali, it's great to have you on the Cube. Thank >>you, Lisa. Get great to be here and be part off AWS treatment. Exciting times. >>They are exciting times. That is true. No, never a dull moment these days, right? So all he talked to me a little bit. About what? A w what BMC is doing with AWS. Let's dig into what you're doing there on the technology front and unpack the benefits that you're delivering to customers. Great >>questions, Lisa. So at BMC, we really have a close partnership with AWS. It's really about BMC. Placido Blue s better together for our customers. That's what it's really about. We have a global presence, probably the largest, uh, off any window out there in this in our industry with 15 data centers, AWS data centers around the globe. We just announced five more in South Africa. Brazil Latin Um, a P J. A couple of them amia across the globe. Really? The presence is very strong with these, uh, data centers because that lets us offered local presence, Take care of GDP are and we have great certification. That is Aw, sock to fedramp. I'll four Haifa dram. We even got hip certifications as well as a dedicated Canada certifications for our customers. Thanks to our partnership, close partnership with the WS and on all these datas into the cross. In addition, for our customers, really visibility into aws seamless capability toe do multi cloud management is key and with a recent partnership with AWS around specifically AWS >>s >>S m, which gives customers cream multi cloud capabilities around multi cloud management, total visibility seamlessly in AWS and all their services whether it's easy toe s s s three sage maker, whatever services they have, we let them discover on syphilis. Lee give them visibility into that. >>That 360 degree visibility is really key to understand the dependencies right between the software in the services and help customers to optimize their investments in a W s assume correct. >>Exactly. With the AWS s s m and r E I service management integration. We really give deep visibility on the dependency, how they're being used, what services are being impacted and and really, AWS s system is a key, unique technology which we've integrated with them very, very happy with the results are customers are getting from it. >>Can you share some of those results? Operational efficiencies, Cost savings? Yeah, >>Yeah, least another great question. So when I look at the general picture off E I service management in the eye ops, which we run with AWS across all these global dinner senses and specifically with AWS S S M people are able to do customers. And this is like the talkto hyper scale, as we're talking about, as well as large telcos like Ericsson and and some of the leading, uh, industry retail Or or, you know, other customers we have They're getting great value because they're able to do service modeling, automatically use ascend to get true deep visibility seamlessly to do service discovery with for for for all the assets that they run or using our S service management in the eye ops capabilities. It really is the neck shin and it's disrupting the service idea Some traditional service management industry with what we offering now with the service management, AWS s, S M and other AWS Cloud needed capabilities such as sage Maker and AWS, Lex and connect that we leverage in our AI service management ai absolution. We recently announced that as a >>single >>unified platform which allows our customers to go on BMC customers and joined with AWS customers to go on this autonomous digital enterprise journey Uh, this announcement was done by our CEO of BMC. I'm in Say it in BMC Exchange recently, where we basically launched a single lady foundation, a single platform for observe ability, engagement with automation >>for the autonomous digital enterprise. I presume I'd like to understand to, from your perspective, this disruption that you're enabling. How is it helping your customers not just survive this viral disruption that we're all living with but be able thio, get the disability into their software and services, really maximize and optimize their cloud investments so that their business can operate well during these unprecedented times, meet their customer demands, exceed them and meet their customers. Where? There. How is this like an accelerator of that >>great question, Lisa. So when we say autonomous digital enterprise, this is the journey All our customers they're taking on its focus on three trips, agility, customer center, city and action ability. So if you think about our solutions with AWS, really, it's s of its management. AI ops enables these enterprises to go on this autonomous digital enterprise journey where they can offer great engagement to the employees. All CEOs really care about employee engagement. Happy employees make for more revenue for for those enterprises, as well as offer great customer experience for the customers. Uh, using our AI service management and AI ops combined. 80 found in this single platform, which we are calling 80 foundation. >>Yeah, go ahead. Sorry. >>No, go ahead, please. >>I was going to say I always look at the employee experience, and the customer experience is absolutely inextricably linked with the employee experience is hampered. That's bride default. Almost going to impact the customer experience. And right now, I don't know if it's even possible to say both the employee experience and the customer experience are even mawr essential to really get right because now we've got this. You know this big scatter That happened a few months ago with some companies that were completely 100% on site to remote being able, needing to give their employees access to the tools to do their jobs properly so that they can deliver products and services and solutions that customers need. So I always see those two employees. Customer experience is just inextricably linked. >>Absolutely. That's correct, especially in this time, even if the new pandemic these epidemics time, uh, the chief human resource offers. The CEOs are really thick focused on keeping the employees engaged and retaining top talent. And that's where our yes service management any other solution helps them really do. Use our digital assistance chat boards, which are powered by a W X and Lex and AWS connect and and and our integration with, uh, helix control them, which is another service we launched on AWS Helix Control them, which is our South version off a leading SAS product automation product out there, a swell as RP integrations we bring to the table, which really allows them toe take employing, give management to the next level And that's top of mind for all CEOs and being driven by line of business like chief human resource officers. Such >>a great point. Are you? Are you finding that mawr of your conversations with customers are at that sea level as they look to things like AI ops to help find you in their business that it's really that that sea level not concerned but priority to ensure that we're doing everything we can within our infrastructure, wherever where our software and services are to really ensure that we're delivering and exceeding customer expectations? That a very tumultuous time? >>Yes, What we're finding is, uh, really at the CEO level CEO level the sea level. It's about machine learning ai adopting that more than the enterprise and specifically in our capabilities when I say ai ops. So those are around root cause predictive I t. And even using ai NLP for self service for self service is a big part, and we offer key capabilities. We just did an acquisition come around, which lets them do knowledge management self service. So these are specific capabilities, predictability, ai ops and knowledge management. Self service that we offer that really is resonating very well with CEOs who are looking to transform their I T systems and in I t ops and align it with business is much better and really do innovation in this area. So that's what's happening, and it's great to see that we will do that. Exact capabilities that come with R E Foundation. The unified platform forms of ability and lets customers go on this autonomous digital enterprise journey without keeping capabilities. >>Do you see this facilitating the autonomous digital enterprise as as a way to separate the winners and losers of tomorrow as so much of the world has changed and some amount of this is going to be permanent, imagine that's got to be a competitive advantage to customers in any industry. >>We believe enterprises that have the growth mindset and and want to go into the next generation, and that's most of them. Toe, to be honest, are really looking at the ready autonomous digital price framework that we offer and work with our customers on the way to grow revenue to get more customer centric, increase employee engagement. That's what we see happening in the industry, and that's where our capabilities with 80 Foundation as well as Helix. Whether it's Felix Air Service management, he likes a Iot or now recently launched Helix Control them really enable them toe keep their existing, uh, you know, tools as well as keep their existing investments and move the ICTY ops towards the next generation off tooling and as well as increase employee engagement with our leading industry leading digital assistant chat board and and SMS management solution that that's what we see. And that's the journey we're taking with most of our customers and really, the ones with the growth mindset are really being distinguished as the front runs >>talk to me about some validation from the customer's perspective, the industry's perspective. What are you guys hearing about? What you're doing s BMC and with a w s >>so validation from customer that I just talked about great validation. As I said, talk to off the hyper skills users for proactive problem management. Proactive incident management ai ops a same time independent validation from Gardner we are back wear seven years and I don't know in a row So seven years the longest street in Gartner MQ for I t s m and we are a leader in that for seven years the longest run so far by any vendor. We are scoring the top in the top number one position in 12 of the 15 critical capabilities. As you know, Gardner, I d s m eyes really about the critical capability that where most customers look. So that's a big independent validation. Where we score 12 off the way were number one in 12 of the 15 capability. So that was the awesome validation from Gardner and I. D. S M. We also recently E Mei Enterprise Management Associates published a new report on AI Ops and BMT scored the top spot on the charts with Business impact and business alignment. Use cases categories for AI ops. So think about what that means. It's really about your business, right? So So we being the top of the chart for business impact and business alignment for ai ops radar report from Enterprise Management associated with a create independent validation that we can point toe off our solutions and what it is, really, because we partner very closely with our customers. We also got a couple of more awards than we want a lot more, but just to mention two more I break breakthrough, which is a nursery leading third party sources out there for chat boards and e i base chat board solution lamed BMC Helix Chat Board as the best chat board solution out there. Uh, SAS awards another industry analysts from independent from which really, uh really shows the how we're getting third parties and independents to talk about our solutions named BMC SAS per ticket and event management, which is really a proactive problem and proactive incident solution Revolution system as as the best solution out there for ticketing and event management. >>So a lot of accolades. A. Yes. It sounds like a lot of alcohol. A lot of validation. How do customers get How do you get started? So customers looking to come to BMC to really understand get that 3 60 degree visibility. How did they get started? >>Uh, well, they can start with our BMC Discovery, which integrates very tightly with AWS s s M toe. Basically get the full visibility off assets from network to storage toe aws services. Whether there s three. Uh, easy to, uh doesn't matter what services they did. A Kafka service they're using whatever. So the hundreds of services they're using weaken seamlessly do that. So that's one way to do that. Just start with BMC Helix Discovery. Thea Other one is with BMC Knowledge Management on BMC Self Service. That's a quick win for most of our customers. I ai service management, tooling That's the Third Way and I I, off stooling with BMC, Helix Monitor and AI ops that we offer pretty much the best in the industry in those that customers can start So the many areas, and now with BMC, control them. If they want to start with automation, that's a great way to start with BMC control them, which is our SAS solution off industry leading automation product called Controlling. >>And so, for just last question from a go to market perspective, it sounds like direct through BMC Channel partners. What about through a. W. S? >>Yes, absolutely. I mean again, we it's all about BMC and AWS better together we offer cloud native AWS services for our solutions, use them heavily, and I just mentioned whether that S S M or chat boards or any of the above or sage maker for machine learning I and customers can contact the local AWS Rep toe to start learning about BMC and AWS. Better together. >>Excellent. Well, Ali, thank you for coming on the program, talking to us about what BMC is doing to help your customers become that autonomous digital enterprise that we think up tomorrow. They're going to need to be to have that competitive edge. I've enjoyed talking to you >>same year. Thank you so much, Lisa. Really. It's about our customers and partnering with AWS. So very proud of Thank you so much. >>Excellent for Ali Siddiqui. I'm Lisa Martin and you're watching the Cube.

>>Hi, I'm Peter Burress. And welcome back to know the Cube conversation. This one from B M sees Helix Immersion Day at Santa Clara Marriott in Santa Clara, California. Once again, we've got a great set of topics for today Today, Right now we're gonna talk about is the everybody talks about the explosion in the amount of data, but nobody talks about the resulting or associated explosion in software. And that may in fact, be that an even bigger issue than the explosion and data. Because ultimately, we want to apply that data and get work done. That's gonna require that we rethink service's rethink service management, rethink operations and rethink operations management in the context of how all this new software is gonna create new work but also can perform new classes of work. Soto have that conversation. We've got a couple of great guests. New York. And here is the BMC president of Digital Service is in operations management division to BMC. Welcome back to the Cube. >>Thank you. >>And me Here shoot Close the CEO of Automation anywhere here. Welcome to the Cube. So Naoki, I want to start with you. A year ago, we started on this journey of how this new digital service is is going to evolve to do Maur types of work for people. How has be emcees? Helix Platform evolved in that time. >>So if you remember last time, it's almost a year. Back when we launched Helix, which was all around taking the service management capability that we had on Prem Minute available in cloud continue rise so customers can run and cut of their choice and provided experience through various channels bought as channel off that customer experience. This is what we had released last time. We call it the three C's for Helix, Everything in cloud containerized with cognitive capabilities so customers can transform that experience in this version. What we are extending helix is with the operation side. So although I Tom capabilities that we have in our platform are now a part off Felix, so we have one entering platform so that customers can discover every asset that they have on prominent loud monitor those assets detected anomalies service bought four lines of business and for i t. For immediate issues that happen, vulnerabilities that are there in the system and automatically optimized capacity and cost on holistic. This whole closed loop off operations and service coming together is what this next day off innovations that were launching BMC Helix >>Soma here New York He's talked about very successfully, and Felix has been a very successful platform for improving user experience. But up front, I noted that we're not just talking about human beings as users anymore. We're talking about software is users R p a robotic process. Automation is a central feature of some of these new trends. Tell us a little bit about how robotic process automation is driving an increased need for this kind of digital service in operations management capability? >>Sure think it a high level you have to think of. The new organization has augmented organization that are human and what's working side by side, each doing what they're best at. And so, in a specific example of a service organization, uh, the the BMC hell ex ist Licht Alexis Taking this is Think of this as a utility where the way you plug it into an electricity outlet and switch on the light and you get the electricity, you plug into the BMC helix, and behind it, you have augmented workforce of chat boards are pia bots, human beings each doing what they're best at and giving a far superior customer experience and like any other that is happening now. And that's the future off service industry. >>But when you point a human, so to speak metaphorically into that system, there's a certain amount of time there's a certain amount of training. There's a certain, and as a consequence, you can have a little bit more predictable scale. That doesn't mean that you don't end up with a lot of complexity, but our p A seems that the potential of our P A seems that you're going to increase the rate at which these users, in this case, digital users are going to enter into the system. You don't have a training regimen you can attach to them. They have to be tested. They have to be discovered. You have to be put in operation with reliability. How is that ultimately driving the need for some of these new capabilities? >>I think you if you think of this, if you think of this box as a digital workers, you almost have to go through the same process that you would go through human beings. You onboard them in terms of you, configure them. You trained them with cognitive capabilities and the and then in. The one difference is the monitor themselves. Without any bias they give, they can give you. They can give their own performance rating performance rating card. Um, but the beauty off this is when human and what's work together because there are some functions that the bots can do well. And then at some point they can hand off to the human beings and human beings. Do some of the more interesting work that is based on judgment. Call customer service. All of that, um, so that the combination is is the end goal for everybody >>and to add would be here said right, that customer experience, whether you're providing experience to employees, are consumers and customers. That is the ultimate goal. That's ultimate result of what you want to get and the speed at which you provided experiences, the accuracy of which you provide experience of the cause, that which you provided experience becomes a competitive sensation, which is where all this automation, this augmentation that they're doing with humans and bots is what enables us to do that right for or large enterprise customers May major service organizations trying to transform into that beautiful. >>But increasingly, it seems as though the, uh, the things that we have to do to orchestrate in ministry Maur users digital and human undertaking Maur complex tasks where each is best applied is really driving a lot of new data mentioned upfront, an enormous amount of software and you said new experiences. But those experiences have to be reliable, have to be secure. They have to be predictable. So that suggests this overwhelming impact of all of these capabilities. You talk about a digital tsunami? What are some of the key things? Do you think Enterprise is gonna have to do to start engaging that? >>Yeah, I'm incredibly college 40 nursery revolution. Whether we call our initial transformation, I think what we all are experiencing is the tsunami Texan ami, right, Tsunami of clouds, where you have corruption clouds, private clouds have a close marriage clouds, tsunami of devices, not just more valid visors, but also has everything alone, as is getting connected devices, tsunami of channels. I mean, as an end user, I wantto experience that in the channel of my preference lack as a journalism as a channel tsunami of bots, off conversation, bullets in our Peabody. So in this tsunami, I think what everyone is trying to figure out is, how do they manage this explosion? It's humanly impossible to do it all manually. You have toe augment it. But of course, intelligence, I'm all. But then, of course, boss, become a big part of that augmentation toe. Orchestrate all of them back to back cross. >>I would say that the this is no longer nice to have, because if you look it from over consumer's perspective, last 20 years of digital technologies off from my Amazons and Google's of the World, Netflix and others they have created this mind set off instant customer gratification, and we all been trained for it. So what was acceptable five years ago is no longer acceptable in our own lives, I e. And so this new standard off instant result instant outcome. Instant respond. Instant delivery V. Just expected. Right. Once you're end, consumer begins to do that. We as a business is no longer have a choice that's writing on the wall. And so what? This new platform Zehr doing like you'd be emcee. Hellickson automation anywhere is delivering their instant gratification. And when you think about it, more and more of the new customers that are millennials, they don't know any other way. So for them, this is the only experience they will relate. Oh, so again, this is not nice to see Oh, it is. But it is the only way only the world will operate, right? >>Well, what we're trying to do is take on new classes of customer experience, new operational opportunities to improve our profitability, innovate and find new value propositions. But you mentioned time arrival rate of transaction is no longer predictable. It's gonna be defined by the market, not by your employees. We could go on and on and on with that. What is taught us a little bit about automation anywhere and what automation anywhere is doing to try to ensure that as businesses go off to attend to the complexity creates new value at the same time can introduce simplicity where they could get scale and more automation. >>Sure, you earlier mentioned that with explosion of data came the explosion off applications And what? Let me focus on what problem or permission anywhere solves. If you look at large organizations, they have vast amount of applications, sometimes 408 100 few 1000 what we have seen. What we've been doing historically is using people as a human bridges between this applications. And we have a prettier that way for too long. And that's the world today. >>So humans are the interface >>humans at the bridges between applications and often called the salty air operations. That's the easiest way to describe it. So the what are two mission ever does is it offers this technology platform robotic process automation area in an Arctic split form that integrates all off it together into a seamless automation bought that can go across and with the eye it can make intelligent, intelligent choices. Um, and so now take that Combined with the BMC, Alex, and you have a seamless service platform that can deliver superior experience. >>So we've got now these swivel chair users now being software, which means that we could discover them more easily. We can monitor them more easily, and that feeds. He looks >>absolutely so you know, in our consumer wall, in a day to day life We are used to a certain experience of how we consume data or consume experiences with our TVs and all the channels that experience that we have an identity. Life is what people expect when they walk into the company, right walking to the Enterprise, which every IittIe organization is trying to figure out. How do they get to that level of maturity? So this is what the combination of what we're doing with Felix and automation anywhere brewing's that consumer great experiences into an enterprise >>world. Some here when we think about our p A. We're applying it in interesting and innovative ways, no question about it. But there are certain patterns of success. Give us some visibility into what you are seeing leads to success. And then what's the future of our P? A. How's that gonna involve over the next few years? >>Sure. Um, R P has been deployed across virtually every industry and virtually every department, so there are many ways to get started in All of them are right. But often we find is that you can either start in a central organization where in terror organization is doing everything centrally. It is a great way to get started. But eventually we learned that the Federated Way is the best way to end where hundreds of offices all over the world, if you are especially large organization, each business unit is doing it with I t providing governments and central security and policies and an actual bots running and being implemented all over the world eventually for a large gilt transformation. That is a common pattern we have seen among successful customers. >>And where do you think this is? Houses pattern going to evolve as enterprises gained more familiarity with it, innovating new and interesting ways and his automation anywhere, and others advance the state of the art. Where do you think it's gonna end up? >>The read is going is is I define it as an app store experience or a Google play experience. So if you think about how we operate over mobile devices today, if you want something on your device, you would look for a nap that does that. We're getting to a point where there is bought for everything in a digital worker for everything. So if you need certain job done, you first go to a what store? Uh that is an automation anywhere website. Look for about that. Does something higher or download that Bart. Get the work done and it comes pre built. Like many. There are works with BMC Felix on many of those, So s. So that is your 1st 1st way you will look, look for getting your work done in a new body economy. And if it if there's no but available, then you look for other options. It will transform how we work and how we think of >>work. In many respects, it's the gig economy with perfect contractor, and it's that leads to some very in string challenges. Ultimately, we start thinking about service Is so Ni aki based on what me here just talked about. Where does digital service is go as our P A joins other classes of users in creating those new experiences at new Prophet points and new value propositions, >>it becomes a competitive. How you provide that service can become a big competitive sensation for financial institutions. For telcos, which is a service industry, right, you're providing that service and, like two meters point, then the user hits that switch. They expect the light to come on If I'm an end user, that consumer warning a service from my telco provider, all from my, um, financial institution. I expect that service to be instantaneous at the highest accuracy accuracy at which super wide is gonna start driving competitor, official for financial institutions of financial institution Telco two Telco and that So I C companies, differentiating and really surviving are thriving in the long term. >>It's no longer becoming something that's nice to have its jacks or better in business, too. >>That's right. And the demo of the live demo that we saw today was really impressive because it sure that what would have taken a few days to happen now happens in three minutes. Right? It is, which is, which is almost the time it takes to call an uber. You know, when interpreters begin to do work at a pace that what you call an uber that's that's that's the future. Yes, it's here. >>Yes, so do I mean the demo that we do the entire enter and demo to request additional storage and being able to provisional remediating issues that we see predict cost and make it available to the end user develop whoever it is is asking for it in minutes. Alright, which used to take days and days. No, no, no, not to mention sometimes in pixels. >>It's typically done faster at scale, with greater reliability. Greater greater security, Certainly greater predictability, et cetera. All right. Here. Shukla, CEO of automation Anywhere. Yeah. Kenny, our president off the dental Service is and operations management division at BMC. Thanks both of you for being on the Cube. >>Thank you. >>Thank you. >>Once again, I'm Peter Burress and I want to thank you for participating in this cube conversation from Santa Clara Marriott at B M sees helix immersion days until next time.

>> from our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to this. Keep conversation here in Palo Alto, California. Keep studios. I'm John for the host of the Cube were with Cuba Lum nine. Special gas *** while the president of products and marking it in from Attica. I make great to see you has been a while, but a couple months. How's things good to be >> back has always >> welcome back. Okay, so in dramatic, a world's coming up. We have a whole segment on that, but we've been covering you guys for a long, long time. Data is at the center the value proposition. Again and again, it's Maur amplified. Now the fog is lifting. Show in the world is now seeing what we think we were told about four years ago with data. What's new? What's that? What's the big trends going on that you guys air doubling down on what's new? What's changed? Here's the update. Sure, >> I think we've been talking for the last couple of years. I think you're right. It is becoming more and more important. I think three things we see a lot one is. Obviously you saw this whole world of district transformation. I think that definitely has picked up so much steam. Now. I mean, every company's going digital and And that the officer, that creates a whole new paradigm shift for companies to come almost recreate themselves remained. And so that data becomes the new definition. And that's what we call the thing is you side and fanatical even before the data three dollar word. But data is the center of everything, right? And in basically see the volume of data growth, you know, the utilization of data to make decisions, whether it's, you know, a decision on the shop floor decisions basically related to a cyber security or whatever it is on the keel of your signal is different now. Is the hole e. I assisted data management. I mean the scale ofthe complexity, the scale of growth, you know, multi cloud, multi platform, all the stuff that's in front of us. It's very difficult to run the old way of doing things. So that's where we see the one thing that we see a whole lot is is becoming a lot more mainstream still early days. But it's assisting the whole ability for companies to what I call exploit data to really become a lot more transformative. >> You've been on this for a while again. We get what we had to go back to. The Cube archives were almost pullout clips from two years ago be relevant today. You know the data control understanding. You know that. You know, I understand where the date of governance is ours. So is the foundational thing. But you guys nailed the chat box. You've been doing a Iot of previous announcements. This is putting a lot of pressure on you. The president of products you got. Get this out there. What's new? What's happening inside in from Attica? He's pedaling as fast as you can. What are some of the updates? Give >> us the best example. I was just like the duck, right? You know, you're really selling your Felix comma the top and then you're really finally I think it's great for us. I think I look a tw ee eye ee eye. It's like this so much fun around machine learning. We look at it, it's two different ways. One is how we leverage machine learning Vidin our products to help our customers, making it easy for them to. As I said, so many different data types Think of I ot data instructor data streaming data. How do you bring all that stuff together and married with your existing transaction? It'LL make sense. So we're leveraging a lot of machine learning to make the internal products a lot more easier to consume. A lot more smarter, a lot more. Richard, The second thing is that we what we call his are a clear which we are. Really? If you remember a couple years ago and in America World, how guard then helps our customers make smarter decisions in the in the one of data signs and all these new data workbench is, you know, the old statistical models are only as good as they can never be. So we're leveraging, helping our customers take the value proposition of r B. I clear then what? I make things that, you know, find patterns that, you know, statistical models cannot. So, to me, I look att, both of those really leveraging ml to shape our products, which is married to a lot of innovation and then creating our eclair to that help customers make smarter decisions, easier decisions, complex decisions. Which would I kill the humans or the statistical models? >> Really Well, this is the balance between machines and humans working together. And you guys have nailed this before. And I think this was two years ago. I started to hear the words land adopt, expand from you guys. Write, which is you've got to get adoption, right? And so as you're iterating on this product, focus, you've got to get it working your >> butt looks big, maniacal focus of that. Let's talk about >> what? What you've learned there because that's a hard thing. You guys are doing well at it. We've got to get a doctor. Means you gotta listen to customers going do the course correction. What's the learning is coming out of that. That >> is actually such a good point. We made such. We were always a very customer centric company. But as you said like that, as the world shifted towards a new subscription cloud model, be really focused on helping our customers adopt our products. And you know, in this new world, customers are also struggling with new architectures and everything, so we double down on what we call customer success, making sure we can help our customers adopt the products. And whether it's it's, it's too will benefit. Our customers can value very quickly. And of course, we believe in what we call a customer for life. Our ability to then grow without customers and held them deliver value becomes a lot better, so we're really for So we have globally across the board customers, success managers, we really invest in a customer's. The moment we a customer, buys a product from us, we directly engage with them to help them understand forthis use case. How you >> implement its not just self serving. That's one thing which I appreciate because you know, how hard is it? Build products these days, especially with philosophy, have changed, but it's also we have in the large scale data. You need automation. You've gotta have machine learning. You gotta have these disciplines. Sure this both on your own, but also for the customer. Yes, any updates on the Clare and some customer learnings, and you're seeing that air turning into either use cases or best practices, >> many of them. So take a simple example, right? I mean, we think if we take these things for granted, right? I mean, taking over here to talk about I open these designs on all of these sensors. We were streaming data, right? Or even robots in the shop floor. Sort of. That data has no schema, no structure, nor definition. It's coming like Netflix data has to. And for customers, there's a lot of volume on it. None of it could be junk. Right? So how do you first think that volume of data creates some structure to it for you to do analytics? You You can only do analytics if you put some structure to it. Right. So first thing is that we leverage clear help customers create what are called scheme, and you can create some structure to it. Then what we do allow is basically clear through clear. It can naturally bring what we have. The data quality on top of it. Like how much of it is irrelevant? How much of it is noise? How much would it really make sense? So then what was you said? It signal from the noisy were helping customers get signal from the noise of data. That's where it becomes very handy because It's a very man will cumbersome, time consuming and something very difficult to do. So that's an area of every have leveraged, creating structure, adding data quality on top and finding rules that didn't probably naturally didn't exist, that you and he would be able to see machines are able to do it. And to your point, our belief is this is my one hundred percent believe we believe in the eye assisting the humans. We have given the value ofthe Claire, tow our users that it compliments you. And that's where we're trying to help our users get more productive and deliver more value faster. >> Productivity is multifold. It's like also efficiency. You don't want people wasting time on project that can be automated. You focus that valuable resource somewhere else. Yeah, okay, so let's shift gears on. Taking from Attica World coming up. Let's spend some time on that. What's the focus this year? The show. It's coming up right around the corner. What's going to focus on what's going to be the agenda? What's on the plate >> give you a quick sense of how it's the shape of its going to be our biggest in from Attica well, so it's twentieth year again. Back in Vegas, you know we love Vegas. Of course, we have obviously a couple of days line up over there and you guys will be there too Great sort of speakers. So obviously we'LL have mean stage speakers like so we'LL have some CEO of Google Cloud Thomas Korean is going to be there We'LL have on main stage with Neil We'LL have the CEO of dealer Breaks Ali with me We'LL also have the CMO off a ws ariel there. Then we have a couple of customers lined up Simon from Credit Suisse Daniels CD over Nissan. We also have the head of the eye salmon Guggenheimer from Microsoft, as well as the chief product officer of Tableau Francois on means. So we have a great lineup of speakers, customers and some of our very, very strategic partners with us. Remember last year we also had Scott country. That means too eighty plus session's pretty much a ninety percent led by customers. We have seventy to eighty customers. Presentable sessions, technical business. We have all kinds of tracks. We have hands on labs. We have learnings. Customers really want to come. Lana products. Talked to the experts someone to talk to the product manager. Someone talk to the engineers literally, so many hands on lab. So it's going to be a full blown a couple of days. What's >> the pitch for someone watching that has never been in from Attica world? Why should they come for the show? >> I always tell them three things. Number one is that it's a user conference for our customers to known all things about data management. And then, of course, in that context, they learned a lot about so they learned a lot about the industry. So Dave one we kicked around by market perspective giving Assessor the market is going, how everybody should be stepping back from the data and understanding. Where are these district transformation? E I? Where is the world of detail going? We have some great analysts coming, talking, some customers talking. We'LL be talking about futures over there. Then it is all about hands on learning, right, learning about the product hearing from some of these experts, right from the industry experts as well as our customers teaching what to do, what not to do and networking. It's always great to network writes a great place for people to learn from each other. So it's a great forum for for two of those three things. But the team this year is all around here. I talked about clear. In fact, our tagline Dissidents, clarity unleashed. I really want to, basically has been developing for the last couple of years. It's become becoming a lot who means stream for us in our offerings. And this year we really are taking it being stream. So it's kinda like unleashing it where everybody can genuinely use a truly use it from the data data management. Active >> clarity is a great team. I mean plays on Claire, But this is what we're starting to see. Some visibility into some clear economic benefits, business benefits, technical benefits, kind of all starting to come in. How would you categorize those three years? Because, you know, that's generally the consensus these days is that what was once a couple years ago was like foggy. When you see now you're starting to see that lift. You see economic, business and technical benefits. >> To me, it's all about economic and business. Anniversary technology plays a role in driving value for the business, my gramophone believing that right? And if you think about some of the trans today, right, ah, billion users are coming into play. That he be assisted by data is doubling every year. You know, the volume of data and and amount ofthe amount off. And I obviously business users today. I mean, when I run a business I want, I always say, tomorrow's data yesterday to make a decision. Today it's just in time, and that's where it comes into play. So our goal is to help organizations transformed themselves truly, you know, be more productive, produce operational cost by the government and compliance that's becoming such a mainstream topic. It's not just basically making analytical decisions. How do you make sure that your data is safe and secure? You don't want to get basically hit by any of these cyberattacks. They're all coming after data. So governance and compliance of data that's becoming but in the end got stored on the >> data thing. Yeah, I wanna get your reactions. You mention some shots like some stats here. Date explosion fifteen point three's added bytes per year in traffic, five million business data users and growing twenty billion connected devices. One billion workers will be assisted by learning. So no thanks for putting those stats, but I want to get your reactors. Some of these other points here, eighty percent of enterprises air that we're looking at multi cloud. They're really evaluating their where the data sits in that kind of equation short. And then the other thing is that the responsibility and role of the chief data? Yes, these air new dynamic. I think you guys will be addressing that. And because organizational stuff dynamics, skill, gaps are issues. But also you have multi clouds form. >> And that's a big thing. I mean, look thin. The old World John hatred Unite is always too large in the price is right, and it's going to stay here. In fact, I think it's not just cloud. Think of it this way, one promised. Ilya is not going away. It's producing in school. But then you have this multi cloud world sassafras pass halves infrastructure. If I'm a customer, I want to do all of it. But the biggest problem comes, you said, is that my data is everywhere. How do I make sense of it? And then how do I go on it like my customer data sitting somewhat in this *** up in that platform in this on prime application transaction after running hardware Connect three. And how do I make sense? It doesn't get. I can have a governance and control around it. That's where data management becomes more important but more complex. But that's where it comes into making it easier. One of the things we've seen a lot of you touched upon is the rise of the Sirio. In fact, we have Danielle from the Sanchez, a CD off Mr North America on Main Stage, talking about her rule and how they've leveraged data to transform themselves. That is something we're seeing a lot more because you know, the rule of the city or making sure there is, You know, not only a sense of governance and compliance, a sense of how to even understand the value of dude across an enterprise again. I see one of the things we're gonna talk about this. It's old system thinking around data. We call it system, thinking three daughter data is becoming a platform C. There was always that the hard way earlier, whether it is server or computer. We believe that data is becoming a platform in itself. Whether you think about it in terms of scary, in terms ofthe governance, in terms of e i times a privacy, you have to think of data as a platform. That's the that's the other. But >> I think that is very powerful statement, and I'd like to get your thoughts. You know, we've had many countries. Is on camera off camera around product. Silicon Valley Venture Capital. How come started to create value. One of the old adage is used to be build a platform. That's your competitive strategy. There were a platform company, and >> that was a >> strategic competitive advantage that is unique to the company. And they created enablement. Facebook's a great example. Monetize all the data from users. Look where they are short. If you think about platforms today, Charlie, it seems to be table stakes. Not as a competitive is more of a foundational element of all businesses, not just startups enterprises. This seems to be a common thread. Do you agree with that that platforms were becoming table stakes? Because if we have to think like systems people, whether it's an enterprise show supplier ballistically the platform becomes stable. States that could be on primary cloud. Your reactions >> are gonna agree that I'll say it slightly differently. Yes, I think I think platform is a critical competent for any enterprise when they think of their entire technology strategy because you can't do peace feels otherwise. You become a system integrated over your own right. But it's not easy to be a platform clear itself, right? Because it's a platform player. The responsibility of what you have to offer your customer becomes a lot bigger. So we always t have this intelligent in a platform. Uh, but the other thing is that the rule of the platform is different. It has to be very modeling and FBI driven. Nobody wants to buy a monolithic platform. I don't want as an enterprise it on my own. I'm gonna implement five years a platform you want. It's gonna be like a Lego block. Okay? You It builds by itself, not monolithic, very driven my micro services based And that's our belief that in the new World, yes, black form is very critical for youto accelerate your district transformation journeys or data driven district transformation journeys but the platform better be FBI driven micro services based, very nimble that it's not a precursor to value creation but creates value as you want. It's >> all kind of depends on the customer. Get up a thin, foundational data platform from you guys, for instance. And then what you're saying is composed off >> different continents. For example, you have a data integration platform, then you can do the quality on top. You do. You could do master data management on top. You can provide governance. You can provide privacy. You could do cataloging it all builds its not like Oh my gosh, I have to go do all these things over the course of five years. Then I'LL get value. You gotta create value all along. Today's customers want value like in two months. Three months. You don't wait for a year or >> two years. This is exactly why I think the kind of Operation Storm systems mindset that you're referring to. This is kind of enterprises. They're behaving others the way that you see on premise, thinking around data and cloud multi cloud emerging. It's a systems view of distributed computing with the right block Lego blocks >> that that's what I believe is. That's what we heard from customers. He r I spend most of my time traveling, talking to customers on my way to try to understand what customers want today. And you know some of this late and demand that they have it. They can't sometimes articulate my job. I always end up on the road most of the time just to hearing customers, and that's what they want. They want exactly appoint a platform that Bill's not monolithic, but they don't want the platform. They do want to make it easy for them not to do everything piecemeal. Every project is a data project, whether it's a customer experience project, whether it's the government's project, whether it is nothing else but an analytical. It's a data project, but you don't want to repeat it every time. That's what they want, >> but I know you got a hard stuff, but I want your thoughts on this because I've heard the word workload mentioned so many more times these in the past year. It was a tad cloud of all the cute conversation with a word workload was mentioned to be the biggest fund. Yes, work has been around for a while, but nice seeing more and more workloads coming on. Yeah, that's more important for day that we're close to being tied into the data absolutely, and then sharing data cross multiple workloads. That's a big focus. Perhaps you see that same thing. >> We absolutely see that, Onda. The unique thing that we see also that new work towards getting created and the old workloads are not going away, which is where the hybrid becomes very important. See, these serve large enterprises and their goal is to have an hybrid. So, you know, I'm running a old transaction workload over here. I want to have an experimental workload. I want to start a new book. I want all of them to talk to each other. I don't want them to become silos. And that's when they look to us to say connect the dots for me. You can be in the cloud as an example. Our cloud platform, you know, last time and fanatical will remember we talked about like it wasn't five trillion transactions a month, but it's double that it to pen trillion transaction a month growing like crazy. But our traditional workload is also still there. So we connect the dots for customers. >> I mean, thank you for coming on sharing the insights house. You guys doing well? You got three thousand developers, billions in revenue. Thanks for coming. Appreciate the insight. And looking for Adrian from Attica World. Thank you very much. Meanwhile, here inside the Cuban shot furry with cute conversation in Palo Alto. Thanks for watching.

>> Announcer: Big Data Silicon Valley 2017. >> Hey, welcome back, everybody, Jeff Frick here with The Cube. We are live at the historic Pagoda Lounge in San Jose for Big Data SV, which is associated with Strathead Dupe World, across the street, as well as Big Data week, so everything big data is happening in San Jose, we're happy to be here, love the new venue, if you're around, stop by, back of the Fairmount, Pagoda Lounge. We're excited to be joined in this next segment by, who's now become a regular, any time we're at a Big Data event, a Spark event, Holden always stops by. Holden Karau, she's the principal software engineer at IBM. Holden, great to see you. >> Thank you, it's wonderful to be back yet again. >> Absolutely, so the big data meme just keeps rolling, Google Cloud Next was last week, a lot of talk about AI and ML and of course you're very involved in Spark, so what are you excited about these days? What are you, I'm sure you've got a couple presentations going on across the street. >> Yeah, so my two presentations this week, oh wow, I should remember them. So the one that I'm doing today is with my co-worker Seth Hendrickson, also at IBM, and we're going to be focused on how to use structured streaming for machine learning. And sort of, I think that's really interesting, because streaming machine learning is something a lot of people seem to want to do but aren't yet doing in production, so it's always fun to talk to people before they've built their systems. And then tomorrow I'm going to be talking with Joey on how to debug Spark, which is something that I, you know, a lot of people ask questions about, but I tend to not talk about, because it tends to scare people away, and so I try to keep the happy going. >> Jeff: Bugs are never fun. >> No, no, never fun. >> Just picking up on that structured streaming and machine learning, so there's this issue of, as we move more and more towards the industrial internet of things, like having to process events as they come in, make a decision. How, there's a range of latency that's required. Where does structured streaming and ML fit today, and where might that go? >> So structured streaming for today, latency wise, is probably not something I would use for something like that right now. It's in the like sub second range. Which is nice, but it's not what you want for like live serving of decisions for your car, right? That's just not going to be feasible. But I think it certainly has the potential to get a lot faster. We've seen a lot of renewed interest in ML liblocal, which is really about making it so that we can take the models that we've trained in Spark and really push them out to the edge and sort of serve them in the edge, and apply our models on end devices. So I'm really excited about where that's going. To be fair, part of my excitement is someone else is doing that work, so I'm very excited that they're doing this work for me. >> Let me clarify on that, just to make sure I understand. So there's a lot of overhead in Spark, because it runs on a cluster, because you have an optimizer, because you have the high availability or the resilience, and so you're saying we can preserve the predict and maybe serve part and carve out all the other overhead for running in a very small environment. >> Right, yeah. So I think for a lot of these IOT devices and stuff like that it actually makes a lot more sense to do the predictions on the device itself, right. These models generally are megabytes in size, and we don't need a cluster to do predictions on these models, right. We really need the cluster to train them, but I think for a lot of cases, pushing the prediction out to the edge node is actually a pretty reasonable use case. And so I'm really excited that we've got some work going on there. >> Taking that one step further, we've talked to a bunch of people, both like at GE, and at their Minds and Machines show, and IBM's Genius of Things, where you want to be able to train the models up in the cloud where you're getting data from all the different devices and then push the retrained model out to the edge. Can that happen in Spark, or do we have to have something else orchestrating all that? >> So actually pushing the model out isn't something that I would do in Spark itself, I think that's better served by other tools. Spark is not really well suited to large amounts of internet traffic, right. But it's really well suited to the training, and I think with ML liblocal it'll essentially, we'll be able to provide both sides of it, and the copy part will be left up to whoever it is that's doing their work, right, because like if you're copying over a cell network you need to do something very different as if you're broadcasting over a terrestrial XM or something like that, you need to do something very different for satellite. >> If you're at the edge on a device, would you be actually running, like you were saying earlier, structured streaming, with the prediction? >> Right, I don't think you would use structured streaming per se on the edge device, but essentially there would be a lot of code share between structured streaming and the code that you'd be using on the edge device. And it's being vectored out now so that we can have this code sharing and Spark machine learning. And you would use structured streaming maybe on the training side, and then on the serving side you would use your custom local code. >> Okay, so tell us a little more about Spark ML today and how we can democratize machine learning, you know, for a bigger audience. >> Right, I think machine learning is great, but right now you really need a strong statistical background to really be able to apply it effectively. And we probably can't get rid of that for all problems, but I think for a lot of problems, doing things like hyperparameter tuning can actually give really powerful tools to just like regular engineering folks who, they're smart, but maybe they don't have a strong machine learning background. And Spark's ML pipelines make it really easy to sort of construct multiple stages, and then just be like, okay, I don't know what these parameters should be, I want you to do a search over what these different parameters could be for me, and it makes it really easy to do this as just a regular engineer with less of an ML background. >> Would that be like, just for those of us who are, who don't know what hyperparameter tuning is, that would be the knobs, the variables? >> Yeah, it's going to spin the knobs on like our regularization parameter on like our regression, and it can also spin some knobs on maybe the engram sizes that we're using on the inputs to something else, right. And it can compare how these knobs sort of interact with each other, because often you can tune one knob but you actually have six different knobs that you want to tune and you don't know, if you just explore each one individually, you're not going to find the best setting for them working together. >> So this would make it easier for, as you're saying, someone who's not a data scientist to set up a pipeline that lets you predict. >> I think so, very much. I think it does a lot of the, brings a lot of the benefits from sort of the SciPy world to the big data world. And SciPy is really wonderful about making machine learning really accessible, but it's just not ready for big data, and I think this does a good job of bringing these same concepts, if not the code, but the same concepts, to big data. >> The SciPy, if I understand, is it a notebook that would run essentially on one machine? >> SciPy can be put in a notebook environment, and generally it would run on, yeah, a single machine. >> And so to make that sit on Spark means that you could then run it on a cluster-- >> So this isn't actually taking SciPy and distributing it, this is just like stealing the good concepts from SciPy and making them available for big data people. Because SciPy's done a really good job of making a very intuitive machine learning interface. >> So just to put a fine sort of qualifier on one thing, if you're doing the internet of things and you have Spark at the edge and you're running the model there, it's the programming model, so structured streaming is one way of programming Spark, but if you don't have structured streaming at the edge, would you just be using the core batch Spark programming model? >> So at the edge you'd just be using, you wouldn't even be using batch, right, because you're trying to predict individual events, right, so you'd just be calling predict with every new event that you're getting in. And you might have a q mechanism of some type. But essentially if we had this batch, we would be adding additional latency, and I think at the edge we really, the reason we're moving the models to the edge is to avoid the latency. >> So just to be clear then, is the programming model, so it wouldn't be structured streaming, and we're taking out all the overhead that forced us to use batch with Spark. So the reason I'm trying to clarify is a lot of people had this question for a long time, which is are we going to have a different programming model at the edge from what we have at the center? >> Yeah, that's a great question. And I don't think the answer is finished yet, but I think the work is being done to try and make it look the same. Of course, you know, trying to make it look the same, this is Boosh, it's not like actually barking at us right now, even though she looks like a dog, she is, there will always be things which are a little bit different from the edge to your cluster, but I think Spark has done a really good job of making things look very similar on single node cases to multi node cases, and I think we can probably bring the same things to ML. >> Okay, so it's almost time, we're coming back, Spark took us from single machine to cluster, and now we have to essentially bring it back for an edge device that's really light weight. >> Yeah, I think at the end of the day, just from a latency point of view, that's what we have to do for serving. For some models, not for everyone. Like if you're building a website with a recommendation system, you don't need to serve that model like on the edge node, that's fine, but like if you've got a car device we can't depend on cell latency, right, you have to serve that in car. >> So what are some of the things, some of the other things that IBM is contributing to the ecosystem that you see having a big impact over the next couple years? >> So there's a lot of really exciting things coming out of IBM. And I'm obviously pretty biased. I spend a lot of time focused on Python support in Spark, and one of the most exciting things is coming from my co-worker Brian, I'm not going to say his last name in case I get it wrong, but Brian is amazing, and he's been working on integrating Arrow with Spark, and this can make it so that it's going to be a lot easier to sort of interoperate between JVM languages and Python and R, so I'm really optimistic about the sort of Python and R interfaces improving a lot in Spark and getting a lot faster as well. And we're also, in addition to the Arrow work, we've got some work around making it a lot easier for people in R and Python to get started. The R stuff is mostly actually the Microsoft people, thanks Felix, you're awesome. I don't actually know which camera I should have done that to but that's okay. >> I think you got it! >> But Felix is amazing, and the other people working on R are too. But I think we've both been pursuing sort of making it so that people who are in the R or Python spaces can just use like Pit Install, Conda Install, or whatever tool it is they're used to working with, to just bring Spark into their machine really easily, just like they would sort of any other software package that they're using. Because right now, for someone getting started in Spark, if you're in the Java space it's pretty easy, but if you're in R or Python you have to do sort of a lot of weird setup work, and it's worth it, but like if we can get rid of that friction, I think we can get a lot more people in these communities using Spark. >> Let me see, just as a scenario, the R server is getting fairly well integrated into Sequel server, so would it be, would you be able to use R as the language with a Spark execution engine to somehow integrate it into Sequel server as an execution engine for doing the machine learning and predicting? >> You definitely, well I shouldn't say definitely, you probably could do that. I don't necessarily know if that's a good idea, but that's the kind of stuff that this would enable, right, it'll make it so that people that are making tools in R or Python can just use Spark as another library, right, and it doesn't have to be this really special setup. It can just be this library and they point out the cluster and they can do whatever work it wants to do. That being said, the Sequel server R integration, if you find yourself using that to do like distributed computing, you should probably take a step back and like rethink what you're doing. >> George: Because it's not really scale out. >> It's not really set up for that. And you might be better off doing this with like, connecting your Spark cluster to your Sequel server instance using like JDBC or a special driver and doing it that way, but you definitely could do it in another inverted sort of way. >> So last question from me, if you look out a couple years, how will we make machine learning accessible to a bigger and bigger audience? And I know you touched on the tuning of the knobs, hyperparameter tuning, what will it look like ultimately? >> I think ML pipelines are probably what things are going to end up looking like. But I think the other part that we'll sort of see is we'll see a lot more examples of how to work with certain kinds of data, because right now, like, I know what I need to do when I'm ingesting some textural data, but I know that because I spent like a week trying to figure out what the hell I was doing once, right. And I didn't bother to write it down. And it looks like no one else bothered to write it down. So really I think we'll see a lot of tools that look very similar to the tools we have today, they'll have more options and they'll be a bit easier to use, but I think the main thing that we're really lacking right now is good documentation and sort of good books and just good resources for people to figure out how to use these tools. Now of course, I mean, I'm biased, because I work on these tools, so I'm like, yeah, they're pretty great. So there might be other people who are like, Holden, no, you're wrong, we need to rethink everything. But I think this is, we can go very far with the pipeline concept. >> And then that's good, right? The democratization of these things opens it up to more people, you get more creative people solving more different problems, that makes the whole thing go. >> You can like install Spark easily, you can, you know, set up an ML pipeline, you can train your model, you can start doing predictions, you can, people that haven't been able to do machine learning at scale can get started super easily, and build a recommendation system for their small little online shop and be like, hey, you bought this, you might also want to buy Boosh, he's really cute, but you can't have this one. No no no, not this one. >> Such a tease! >> Holden: I'm sorry, I'm sorry. >> Well Holden, that will, we'll say goodbye for now, I'm sure we will see you in June in San Francisco at the Spark Summit, and look forward to the update. >> Holden: I look forward to chatting with you then. >> Absolutely, and break a leg this afternoon at your presentation. >> Holden: Thank you. >> She's Holden Karau, I'm Jeff Frick, he's George Gilbert, you're watching The Cube, we're at Big Data SV, thanks for watching. (upbeat music)